Artificial intelligence for evaluation of optical coherence tomography images

ABSTRACT

A neural network is trained to segment interferogram images. A first plurality of interferograms are obtained, where each interferograms corresponds to data acquired by an OCT system using a first scan pattern, annotating each of the plurality of interferograms to indicate a tissue structure of a retina, training a neural network using the plurality of interferograms and the annotations, inputting a second plurality of interferograms corresponding to data acquired by an OCT system using a second scan pattern and obtaining an output of the trained neural network indicating the tissue structure of the retina that was scanned using the second scan pattern. The system and methods may instead receive a plurality of A-scans and output a segmented image corresponding to a plurality of locations along an OCT scan pattern.

RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.17/444,806, filed Aug. 10, 2021, which issued as U.S. Pat. No.11,393,094, issued Jul. 19, 2022, which claims the benefit under 35U.S.C. § 119(e) of U.S. Provisional Patent Application No. 62/706,800,filed Sep. 11, 2020, the entire disclosures of which are incorporated,in their entirety, by this reference.

The subject matter of the present application is related to U.S.Provisional Patent Application No. 62/953,827, filed Dec. 26, 2019, theentire disclosure of which is incorporated herein by reference.

The disclosed approach to applying a trained Convolutional NeuralNetwork (CNN) to assist in analyzing interferograms can be used withmany scan patterns, such as one or more of a stop and go trajectory, astar trajectory, a continuous trajectory, or a Lissajous trajectory, asdescribed in PCT/US2019/038270, filed Jun. 20, 2019, published as WO2019/246412 on Dec. 26, 2019, the entire disclosure of which isincorporated herein by reference.

BACKGROUND

Eye health is critical for good vision. There are a variety of diseasesand illnesses of the eye that can diagnosed by measuring changes in thestructure of the eye. Such measurements can also provide indications ofdiseases that affect other organs of a patient. The structure of the eyeincludes a cornea and lens that refract light and form an image on theretina. The retina generates electrical signals in response to the imageformed thereon, and these electrical signals are transmitted to thebrain via the optic nerve. The fovea and macula of the retina have anincreased density of cones in relation to other areas of the retina andprovide sharper images.

Measurements of retinal thickness (RT) over time can be used to diagnoseand monitor the health of the retina, the eye, and the patient. Manypatients who have been diagnosed with retinal vascular diseases andother diseases or conditions have an elevated retinal thickness and aretreated with medications. For example, macular edema is a disease thatoccurs when fluid collects on or under the macula of the retina, andresults in an elevated retinal thickness. Macular edema can be anindication of other diseases, such as diabetes or age-related maculardegeneration, uveitis, blockage of retinal vasculature, and glaucoma,for example. Thus, measurements of retinal thickness and determinationof changes in thickness over time can be used as an indication of achange in eye health and other aspects of patient health.

Measurements of RT over time can also be used to evaluate theeffectiveness of medications or treatments so that modifications can bemade if needed. One way to do this is by making regular measurements ofthe thickness of a patient's retina. One technique used to measure thethickness of the retina is optical coherence tomography (OCT). OCT mayalso be used to generate data that can be used to form images of apatient's retina and its tissue structures. Such images may be used toevaluate the condition of the retina, and by inference, a patient'shealth.

At least some OCT devices include a source of a measurement beam, ascanner to move the beam on a patient's retina in a desired scanpattern, a set of optical elements to generate an interference patternbetween a reference version of the measurement beam and light reflectedfrom the retina, and a detector for detecting the interfering lightwaves. In some examples, an OCT system may also include a processor thatexecutes a set of instructions to operate the scanner so as to move themeasurement beam on the retina. The interference patterns created from aset of scans may be combined to form an image representing the layers orregions of the retina, termed an interferogram. Some interferometersfunction by splitting light from a single source into two beams thattravel in different optical paths, and are then combined again toproduce the interference patterns.

An interferogram may be subjected to further image processing to deriveinformation about the retina, such as a measurement of the retinalthickness (“RT”), retinal hydration and fluid pooling. The retinaincludes layers of cells and tissue, such as the inner limiting membrane(“ILM”) and retinal pigment epithelium (“RPE”) layers. The imageprocessing may be used to more clearly distinguish or segment the twolayers. The measurement of RT over time may be used to diagnose illnessor disease, such as by detecting evidence of fluid buildup or fluidpooling in the eye.

Although the detection of fluid pooling in and around the retina wouldbe helpful, work in relation to the present disclosure suggests that theprior approaches can be less than ideal in at least some respects. Forexample, subtle changes in the gray scale values corresponding to a poolof fluid in an OCT image can be difficult for a health care professionalto detect. Also, prior approaches that rely on high resolution systemsto detect retinal fluid pools can be overly complex and of limitedavailability, such that pooling is detected later than would be ideal inat least some instances.

One method of processing interferogram images is to use a neural networkarchitecture referred to as a convolutional neural network (CNN). A CNNis a form of deep learning network and consists of an input and anoutput layer, as well as multiple hidden layers. The hidden layers of aCNN consist of a series of layers that perform a convolution operationusing a multiplication operation or implementation of a dot product. Theactivation function is commonly a rectified linear unit (RELU) layer andis subsequently followed by additional layers such as pooling layers,fully connected layers, and normalization layers. These are referred toas hidden layers because their inputs and outputs are masked by theactivation function and final convolution. A trained CNN can be used toanalyze an image and perform recognition of specific features. Forexample, a properly trained CNN may be used to identify layers orstructures of an image of a retina in a process referred to assegmentation. This information can then be used to determine ameasurement of retinal thickness or to otherwise evaluate a patient'seye or overall health.

A complication in the image processing is that different OCT systems mayuse different scan patterns when collecting data. This can make itdifficult to compare interferograms obtained using different systems. Itcan also make it difficult to perform image recognition for aninterferogram if there is insufficient data available to properly traina CNN to process that type of scan data. Embodiments of the disclosureare directed to overcoming these disadvantages of conventional methodsof processing interferogram data, individually and collectively.

SUMMARY

The terms “invention,” “the invention,” “this invention,” “the presentinvention,” “the present disclosure,” or “the disclosure” as used hereinare intended to refer broadly to all of the subject matter described inthis document, the drawings or figures, and to the claims. Statementscontaining these terms should be understood not to limit the subjectmatter described herein or to limit the meaning or scope of the claims.Embodiments of the invention covered by this patent are defined by theclaims and not by this summary. This summary is a high-level overview ofvarious aspects of the invention and introduces some of the conceptsthat are further described in the Detailed Description section below.This summary is not intended to identify key, essential or requiredfeatures of the claimed subject matter, nor is it intended to be used inisolation to determine the scope of the claimed subject matter. Thesubject matter should be understood by reference to appropriate portionsof the entire specification of this patent, to any or all figures ordrawings, and to each claim.

In some embodiments, the system and methods may be used to perform imagerecognition and processing on interferogram images obtained from OCTscan data. The image recognition and processing may operate to segmentthe tissue layers of a retina to make them more distinguishable. Thescan data may be the result of moving a measurement beam over a retinain a specific scan pattern. In some embodiments, a model or neuralnetwork, such as a convolutional neural network (CNN) may be trainedusing a set of scan data obtained from performing a set of scans using aradial scan pattern. The training data may also comprise scan dataobtained from a different scan pattern that has been interpolated,extrapolated, resampled, or otherwise processed to more closely resembledata that would be obtained from a radial scan pattern. The other scanpattern may be a scan pattern that comprises a plurality of lobes, forexample. After training, the CNN may be used to recognize or enhance therecognition of layers or structures of the retina, where in someembodiments, the input to the trained CNN is data obtained using thescan pattern with the plurality of lobes that has been interpolated,extrapolated, resampled, or otherwise processed to more closely resembledata that would be obtained from a radial scan pattern.

In some embodiments, the system and methods are directed to obtaining afirst plurality of interferograms, wherein each of the interferogramscorresponds to data acquired by an OCT system performing a scan of aretina using a first scan pattern, annotating each of the plurality ofinterferograms formed from the data acquired using the first scanpattern to indicate a tissue structure of the retina, training a neuralnetwork using the plurality of interferograms and the annotations,inputting a second plurality of interferograms corresponding to dataacquired by an OCT system performing a scan of a retina using a secondscan pattern and obtaining an output of the trained neural network, theoutput indicating the tissue structure of the retina that was scannedusing the second scan pattern.

In some embodiments, the system and methods are directed to receiving aplurality of A-scans corresponding to a plurality of locations along anOCT scan pattern and outputting a segmented image corresponding to theplurality of locations along the OCT scan pattern, the segmented imagecomprising one or more of a boundary of an ILM layer, a boundary of anRPE layer, or a boundary of a pool of fluid within the retina.

In some embodiments, an OCT system may be operated with a specificscanning pattern for the measurement beam to enable the collection ofdata and provide more precise measurement of certain areas of the eye.The scanning pattern may result from moving a mirror that is part of theOCT system in response to a driving signal. The mirror intercepts ameasurement beam generated by a light source and directs the beam tofollow a trajectory that varies with the motion of the mirror, forming apredefined scan pattern. In some embodiments, data collected from usinga scan pattern may be interpolated, extrapolated, resampled, orotherwise processed to obtain data that would be obtained from using adifferent scan pattern. This may assist a physician to better understandconditions in different regions of the eye or to compare scans takenwith different scan patterns as part of monitoring the health of apatient's eyes.

In some embodiments, a swept measurement source may be varied inwavelength while a measurement beam is moved on a scan pattern, with theobtained data being subjected to a transform such as a Fourier transformprior to further processing.

In some embodiments, a processor may execute a set ofcomputer-executable instructions to cause the processor or a device toaccess measurement data detected by a detector that is part of an OCTinterferometer. In some embodiments, the processor may executeinstructions to cause the processing of the accessed data to generatemeasurement data that would result from a different scan pattern. Thismay be used as additional training data for a neural network or as aninput to a trained neural network.

In some embodiments, the processor may execute instructions to access aset of stored data for a plurality of A-scans, where each A-scancorresponds to a retinal pigment epithelium (RPE) and an inner limitingmembrane (ILM) of the retina. The stored data may then be processed toenhance the distinction between the RPE and ILM, and as a result, assistin identifying changes to the retina thickness due to a buildup of fluidor formation of a fluid pocket. In some embodiments, the processing maycomprise use of a trained CNN or other neural network or model tosegment an image formed from a plurality of segmented A-scans.

Although specific reference is made to measuring retinal thickness, theimage processing system and methods disclosed herein will findapplication in many fields, such as microscopy, metrology, aerospace,astronomy, telecommunications, medicine, pharmaceuticals, dermatology,dentistry, and cardiology.

Other objects and advantages of embodiments of the disclosure will beapparent to one of ordinary skill in the art upon review of the detaileddescription and the included figures.

INCORPORATION BY REFERENCE

All publications, patents, and patent applications mentioned in thisspecification are herein incorporated by reference to the same extent asif each individual publication, patent, or patent application wasspecifically and individually indicated to be incorporated by reference.

BRIEF DESCRIPTION OF THE DRAWINGS

The novel features of the invention are set forth with particularity inthe appended claims. A better understanding of the features andadvantages of the present invention will be obtained by reference to thefollowing detailed description that sets forth illustrative embodiments,in which the principles of the invention are utilized, and theaccompanying drawings of which:

FIG. 1 shows a simplified diagram of the human eye;

FIG. 2A shows a perspective view of a binocular OCT device for measuringeyes of a user, in accordance with some embodiments;

FIG. 2B shows a block diagram of the binocular OCT device illustratingvarious components within the handheld unit body, in accordance withsome embodiments;

FIG. 2C shows a schematic of an optical configuration that may beimplemented with the OCT binocular, in accordance with some embodiments;

FIG. 3 shows an example of a scan pattern (termed a “flower” patternherein) that may be used to collect OCT data, in accordance with someembodiments;

FIG. 4 shows a set of interferograms or scans acquired by an OCT usingthe scan pattern or trajectory of FIG. 3 , in accordance with someembodiments;

FIG. 5 shows the scan pattern of FIG. 3 superimposed on a radial scanpattern, data for which may be obtained by interpolation of the dataobtained from the scan pattern of FIG. 3 , in accordance with someembodiments;

FIG. 6 shows how the surface of a patient's eye may be divided intozones or regions for purposes of comparing scan patterns by comparingthe amount of scanning or scan time spent collecting data from eachzone, in accordance with some embodiments;

FIG. 7 shows a process for training a CNN or other form of neuralnetwork to perform a segmentation of an interferogram image, inaccordance with some embodiments;

FIG. 8 shows a set of operations that may be used in a process forgenerating additional training data for use in training a CNN or otherform of neural network as described with reference to FIG. 7 , inaccordance with some embodiments;

FIG. 9 shows an original B-scan based on a radial scan pattern, a resultof applying an image degradation ruleset to that scan pattern togenerate an interferogram, and an interferogram obtained by use of asecond scan pattern, in accordance with some embodiments;

FIG. 10A shows an original interferogram and a segmented interferogramobtained from processing the original interferogram using a trained CNN,in accordance with some embodiments;

FIG. 10B shows an example of the flower pattern scan pattern of FIG. 3that was used to obtain the interferogram of FIG. 10A, including anindication of the portion of the scan pattern that generated theindicated section of the interferogram;

FIG. 11A is a flow chart or flow diagram illustrating a process, method,operation, or function for training a neural network using a set of OCTinterferograms obtained using a first scan pattern to determine aretinal tissue structure in a set of OCT interferograms obtained using asecond scan pattern, in accordance with some embodiments;

FIG. 11B is a flow chart or flow diagram illustrating a process, method,operation, or function for generating additional training data fortraining a neural network using a set of OCT interferograms obtainedusing a first OCT system to determine a retinal tissue structure in aset of OCT interferograms obtained using a second OCT system, inaccordance with some embodiments;

FIG. 11C is a diagram illustrating an embodiment in which image dataobtained from a first OCT system and its associated annotations aresubjected to one or more of resampling, degeneration, and augmentationoperations to generate additional training data for use in training amodel that is being trained with image data obtained from a second OCTsystem and its associated annotations;

FIG. 11D is a set of diagrams illustrating an embodiment in whichtraining data obtained from an open access data set of interferograms(retinal images) is subjected to augmentation and degeneration processesto generate training data for a model that is intended to be used withinput data obtained from an OCT system having a lower resolution thanthe OCT system used to generate the interferograms;

FIG. 12 is a diagram illustrating an example of a convolutional neuralnetwork (CNN) architecture that may be used to process an interferogramimage and the output of the CNN representing a segmented image, inaccordance with some embodiments;

FIG. 13 is a diagram illustrating how a set of scan data obtained usingthe flower scan pattern of FIG. 3 may be subjected to further dataprocessing operations (such as interpolation and gaussian blurring) togenerate an image representing a B-scan of a selected cross section of aretina, in accordance with some embodiments;

FIG. 14 is a diagram illustrating further examples of B-scans generatedby processing of data obtained using the flower scan pattern of FIG. 3for different slices through the pattern to create B-scans of differentcross sections of a retina that would be obtained from a raster scan, inaccordance with some embodiments;

FIG. 15 is a diagram illustrating further examples of B-scans generatedby processing of data obtained using the flower scan pattern of FIG. 3for different slices through the pattern to create B-scans of differentcross sections of a retina that would be obtained from a radial scan, inaccordance with some embodiments;

FIG. 16 is a diagram illustrating how a set of the created B-scans ofdifferent cross sections of a retina may be combined to produce a 3Dvisualization or thickness map of a retina, in accordance with someembodiments;

FIG. 17A is a diagram illustrating a comparison of the performance of aconventional scan pattern and data processing method to the resultsobtained using the flower scan pattern and image processing using thetrained CNN described herein, in accordance with some embodiments; and

FIG. 17B is a diagram illustrating a curriculum training process inwhich image data and/or annotations obtained from a first and a secondOCT device are used for training over a set of training iterations, withsome of that data subjected to degeneration.

DETAILED DESCRIPTION

The subject matter of embodiments of the present disclosure is describedherein with specificity to meet statutory requirements, but thisdescription is not intended to limit the scope of the claims. Theclaimed subject matter may be embodied in other ways, may includedifferent elements or steps, and may be used in conjunction with otherexisting or later developed technologies. This description should not beinterpreted as implying any required order or arrangement among orbetween various steps or elements except when the order of individualsteps or arrangement of elements is explicitly noted as being required.

Embodiments of the present disclosure will be described more fullyherein with reference to the accompanying drawings, which form a parthereof, and which show, by way of illustration, exemplary embodimentsmay be practiced. The embodiments disclosed herein may, however, beembodied in different forms and should not be construed as limited tothe embodiments set forth herein; rather, these embodiments are providedso that this disclosure will satisfy the statutory requirements to thoseskilled in the art.

Among other things, the embodiments of the present disclosure may beembodied in whole or in part as a system, as one or more methods, or asone or more devices. Embodiments may take the form of a hardwareimplemented embodiment, a software implemented embodiment, or anembodiment combining software and hardware aspects. For example, in someembodiments, one or more of the operations, functions, processes, ormethods described herein may be implemented by one or more suitableprocessing elements (such as a processor, microprocessor, CPU, GPU, TPU,controller, etc.) that is part of a client device, server, networkelement, remote platform (such as a SaaS platform), or other form ofcomputing or data processing system, device, or platform.

The processing element or elements may be programmed with a set ofexecutable instructions (e.g., software or computer-executableinstructions), where the instructions may be stored in or on a suitablenon-transitory data storage element. In some embodiments, one or more ofthe operations, functions, processes, or methods described herein may beimplemented by a specialized form of hardware, such as a programmablegate array, application specific integrated circuit (ASIC), or the like.Note that an embodiment of the inventive methods may be implemented inthe form of an application, a sub-routine that is part of a largerapplication, a “plug-in”, an extension to the functionality of a dataprocessing system or platform, or any other suitable form. The followingdetailed description is, therefore, not to be taken in a limiting sense.

While various embodiments have been shown and described herein, it willbe obvious to those skilled in the art that such embodiments areprovided by way of example only. Numerous variations, changes, andsubstitutions may occur to those skilled in the art without departingfrom the present disclosure. It should be understood that variousalternatives to the embodiments described herein may be employed. Forexample, although reference is made to measuring a thickness of a samplesuch as the retina, the methods and apparatus disclosed herein can beused to measure many types of samples, such as other tissues of the bodyand non-tissue material. While reference is made to generating maps ofretinal thickness, the methods and apparatus disclosed herein can beused to generate images of retinal samples, such as cross sectional ortomographic images.

The presently disclosed systems, methods and apparatuses are well suitedfor combination with prior images and imaging systems, such as OCTimaging systems and OCT images, in order to provide improvedclassification of image structure, such as tissue type, fluid pooling,etc. In some embodiments, transfer learning is used, in which anartificial intelligence model, e.g. a neural network, trained in a firstsetting is used to improve performance in a second setting. In someembodiments, the first setting comprises a first OCT systemconfiguration comprising a first resolution and a second OCT systemconfiguration, in which the first OCT system configuration comprises agreater resolution (e.g. resolves smaller image details) than the secondOCT system configuration. The transfer learning can be configured inmany ways in accordance with the present disclosure. In someembodiments, the coefficients of the neural network are generated bytraining the neural network on the first data set from the first settingand the learned parameters are then transferred to the second setting,e.g. parameters generated from data from the first OCT systemconfiguration are applied to data from the second OCT systemconfiguration to analyze data from the second OCT system configuration.Alternatively or in combination, the transfer learning may comprisecurriculum learning, in which images of increasing difficulty are usedto train the neural network. In some embodiments, images from the firstsetting corresponding to the first OCT system configuration areprogressively degenerated and used to train the neural network until theimage quality, e.g. resolution, corresponds to images from the secondsetting corresponding to the second OCT system.

An examples of a suitable higher resolution system includes theSpectralis® OCT System commercially available from Heidelbergengineering. An example of a suitable personal biometry system (PBOS)having a lower resolution OCT imaging system is described in U.S. Pat.No. 10,610,096, granted on Apr. 4, 2020, entitled “MINIATURIZED MOBILE,LOW COST OPTICAL COHERENCE TOMOGRAPHY SYSTEM FOR HOME BASED OPHTHALMICAPPLICATIONS”, the full disclosure of which is incorporated herein byreference. The higher resolution OCT system may comprise an axialresolution within a range from about 1 micrometer (um) to about 10 um,and the lower resolution OCT system may comprise an axial resolutionwithin a range from about 15 um to about 50 um, for example. Althoughreference is made to these resolution ranges, in some embodiments, thelower resolution system comprises an axial resolution within the rangeof about 1 um to about 10 um, and the higher resolution comprises aresolution within this range or an even smaller axial resolution, e.g.less than 1 um.

In some embodiments, the systems, apparatuses, and methods described bythis disclosure are directed to identifying structures, regions, orfeatures of images obtained from an OCT system. In some embodiments,this identification may be performed by a trained model, which may takethe form of a neural network. The neural network may be configured oroperate to process an input image and output a segmented image or datathat indicates the probability of each pixel in the input belonging to aspecific class (i.e., the relative probabilities between two classes),with the result being that an image is created that maps each pixel to aspecific class. In some embodiments, the class may be one of astructure, layer, boundary, feature, or pool of fluid in a retina, forexample.

The techniques and methods described herein may be used to perform oneof several tasks or objectives. These include inputting an imageobtained from an OCT system into a trained model and in responseoutputting a segmented image identifying one or more regions, layers,boundaries, feature, pools of fluid, etc. Another task is one ofidentifying a change or progression in a region, layer, boundary,feature, pool of fluid, etc. Yet another task is to compare imagesproduced by two different OCT systems to validate the accuracy of one ofthe systems or to use images obtained from a second OCT system todetermine changes in any regions, etc. identified in the images from thefirst OCT system, where the two OCT systems may have differentresolutions or may employ different scan patterns when collecting imagedata.

For each of the described tasks a trained model may be developed toperform the task. In some embodiments, training a model to perform atask involves applying a machine learning algorithm to a set of data andannotations. The annotations segment an image pixel-wise into two ormore classes and are typically provided by a human being who is familiarwith the subject matter of the images. The machine learning algorithm“learns” the correct label or segmentation to apply to a pixel from thedata and annotations and generates a model in the form of a neuralnetwork.

However, training a model to obtain a desired level of performance(i.e., a desired level of precision and recall, sometimes expressed as aspecific measure) may require more training data than is available. Forexample, there may be sufficient data available from a first type of OCTsystem, or an OCT system with a specific resolution or scan pattern totrain a model, but not enough from a second type of OCT system that isused to generate images that a user would like segmented. As anotherexample, annotations of data from the first device may be more easily orreadily available than annotations of data from the second device. Inthese situations, it would be beneficial to be able to train a modelusing image data obtained from the first type of OCT system and then usethe trained model to classify image data generated by the second type ofOCT system. As mentioned, examples of this situation occur if the twoOCT systems have different resolutions or employ different scan patternswhen collecting image data.

Embodiments comprise data acquisition and processing flows that may beused to produce a trained model for use in image segmentation in asituation where there is a lack of sufficient training data. In suchcases, the (un)availability of sufficient training data may precludetraining a model using the same type of data as generated by a desiredOCT system. In such situations, the techniques and methods disclosedenable the generation of new training data (and in some casesannotations or labels) that may be used in addition to, or as areplacement for, data obtained from a first OCT system when training amodel to perform segmentation of images obtained from a second OCTsystem. In some embodiments, the training data may be from a system witha different (typically higher) resolution, and in some embodiments, thetraining data may be from a system implementing a different scan patternthan the system producing the images to be segmented.

In some embodiments, the potential problems or obstacles caused byinsufficient training data may be overcome by use of one or more dataprocessing techniques described herein. These techniques include: (1)Augmentation—these techniques may be used to generate additionaltraining data by applying one or more operations (e.g., geometricaltransformations, such as those illustrated in FIG. 8 ) to a set of dataassociated with an image (and also in some cases to the associatedannotations of retinal layers, fluid regions, etc.) to provide increaseddata variability for the machine learning algorithm, increase therobustness of the model, and prevent over-fitting of the model to thedata. In some cases, the geometrical transformations may also be appliedto annotations; (2) Degeneration—these techniques are applied tooriginal image data obtained from a OCT system with higher resolution toobtain data that would be expected to be obtained from an OCT systemwith lower resolution; (3) Resampling—this technique is applied to imagedata obtained using a first scan pattern to generate image data expectedto be obtained using a second and different scan pattern (such as istypically produced by a different OCT system); and (4) Registering orregistration—this technique is a way to align annotations or indicationsof features (boundaries, regions, fluid, etc.) in a second set of OCTimages obtained by degenerating a first set of images so that theannotations are correctly associated with the features identified in thefirst set of OCT images.

Embodiments of the system, apparatuses, and methods described by thisdisclosure are directed to the training and use of a model to performthe segmentation of images obtained from an OCT device. In someembodiments, the model is a neural network, such as a convolutionalneural network that may be used for image processing. The output of thetrained neural network is a segmentation of an input image, where thesegmentation operation identifies one or more elements, layers, regions,structures, boundaries, pools of fluid, or other features of a retinathat was imaged by the OCT.

As mentioned, one of the difficulties in developing such a model is thatit requires reliable training data. This problem is made morecomplicated because different OCT systems that might be used to generatetraining data images may have different characteristics, where thesecharacteristics may include scan pattern, axial resolution, lateralresolution, or method of alignment. These differences make it that muchmore difficult to obtain sufficient training data for a model, and alsomake it difficult to compare images obtained using OCT systems withdifferent characteristics or to reliably segment an image obtained usingone type of OCT system using a model trained on data obtained from asecond and different type of OCT system.

FIG. 1 shows a simplified diagram of the human eye. Light enters the eyethrough the cornea 10. The iris 20 controls the amount of light allowedto pass by varying the size of the pupil 25 that allows light to proceedto the lens 30. The anterior chamber 40 contains aqueous humor 45 whichdetermines the intraocular pressure (IOP). The lens 30 focuses light forimaging. The focal properties of the lens are controlled by muscleswhich reshape the lens. Focused light passes through the vitreouschamber, which is filled with vitreous humor 55. The vitreous humormaintains the overall shape and structure of the eye. Light then fallsupon the retina 60, which has photosensitive regions. In particular, themacula 65 is the area of the retina responsible for receiving light inthe center of the visual plane. Within the macula, the fovea 70 is thearea of the retina most sensitive to light. Light falling on the retinagenerates electrical signals which are passed to the optic nerve 80 andthen to the brain for processing.

Several disorders give rise to reduced optical performance of the eye.In some cases, the intraocular pressure (IOP) is either too high or toolow. This is caused, for instance, by too high or too low of aproduction rate of aqueous humor in the anterior chamber or drainage ofaqueous humor from the anterior chamber, for example. In other cases,the retina is too thin or too thick. This arises, for instance, due tothe buildup of fluid in the retina. Diseases related to an abnormalretinal thickness (RT) include glaucoma, macular degeneration, diabeticretinopathy, macular edema and diabetic macular edema, for example. Insome cases, a healthy range of RT is from 175 μm thick to 225 μm thick.In general, abnormalities in either the IOP or the RT or both areindicative of the possible presence of one of several ophthalmologicaldiseases. Additionally, the IOP or the RT vary in response toophthalmological treatments or other procedures. Therefore, it isdesirable to have a means to measure the IOP and/or RT for diagnosis ofophthalmological diseases and to assess the effectiveness of treatmentsfor a given patient. In some cases, it is desirable to measure thethickness of one or more retinal layers, for example the thickness of aplurality of layers. In addition, it is desirable to process dataobtained from an OCT system to assist in identifying fluid pockets orregions in the eye, as these may indicate a change in eye health.

As described, the disclosed OCT system may include a scanner that can becontrolled to cause a measurement beam to move in a scan pattern on apatient's retina. The scan pattern may be one of various types,including a stop and go scan pattern, a star scan pattern, a continuousscan pattern, a Lissajous scan pattern, or a flower pattern, sometimesreferred to as a rose curve. As will be described in further detail, theflower pattern or rose curve may be used to generate measurement datathat can be processed to generate data that represents data that wouldbe obtained from a different scan pattern. Further, the flower patternor rose curve may be used to generate measurement data that can beprocessed to generate interferometric data that can be used as an inputto a trained CNN to provide a segmentation of the layers of the retina.

FIG. 2A shows a perspective view of a binocular OCT device 4900 formeasuring eyes of a user, in accordance with some embodiments. Thebinocular OCT device 4900 comprises a first adjustable lens 4916-1 thatis optically coupled to an OCT measurement system and a first fixationtarget configured within a handheld unit body 4903 (e.g., a housing),both of which are hidden from view in this figure. Similarly, a secondadjustable lens 4916-2 may be optically coupled to the OCT measurementsystem and a second fixation target (hidden). The first adjustable lens4916-1 may be part of a first free space optics that is configured toprovide a fixation target and measure a retinal thickness of the user'seye, whereas the second adjustable lens 4916-2 may be part of a secondfree space optics that is configured to only provide a fixation targetso as to reduce a number of components in the binoculars OCT device4900. For instance, while both free space optics provide the user with afixation target, only one of the free space optics is used to measurethe retinal thickness as the binocular OCT device 4900 may be turnedupside down, i.e. inverted, after the user measures a first eye suchthat the user may measure the other eye.

The binocular OCT device 4900, in this embodiment, comprises aninterpupillary distance (IPD) adjustment mechanism 4905 that isaccessible on the exterior of the handheld unit body 4903. In thisembodiment, the IPD adjustment mechanism 4905 comprises two components,a first component 4905-1 that adjusts the distance between the lenses4916-1 and 4916-2 to match the IPD of a user's pupils when the userplaces the binocular OCT device 4900 front of the user's eyes when theeye cups 4901-1 and 4901-2 rest on the user's face.

This IPD can be set by a healthcare professional and locked intoposition for the user to measure retinal thickness at home.Alternatively, the IPD can be user adjustable. A switch (or other methodof adjustment, such as a screw or dial) 4904 may be used to adjust thelenses 4916-1 and 4916-2 to match a user's refraction, i.e. eyeglassprescription. Alternatively, a mobile device, such as a tablet can beused program the refraction of each eye of the patient. For example, theuser may fixate on the first fixation target with one eye and a secondfixation target with another eye, and the movable lenses adjusted to theuser's refraction. The switch 4904 may selectively adjust the assembliesof the lenses 4916-1 and 4916-2 within the handheld unit body 4903 tochange the positioning of the lenses 4916-1 and 4916-2. These positionscan be input into the device by a health care professional and stored ina processor along with an orientation from an orientation sensor asdescribed herein. The device can be inverted, and the process repeated.Alternatively, or additionally, the prescription for each eye can bestored in the processor and the lenses adjusted to the appropriaterefraction for each eye in response to the orientation of theorientation sensor.

Both of the components 4905-1 and 4905-5 may be implemented as one ormore wheels that the health care professional manually rotates.Alternatively, the IPD adjustment mechanism 4905 may be motorized. Inthis regard, the components 4905-1 and 4905-5 may be configured asdirectional switches that actuate motors within the handheld unit body4903 to rotate gears within the handheld unit body 4903 based on thedirection in which the user directs the switch.

The switch 4904 can be used to adjust the focusing of the binocular OCTdevice 4900. For example, because the focal change effected byadjustment of the lenses 4916-1 and 4916-2 can be measured in acustomary unit of refractive power (e.g., the Diopter) by adjustment ofthe lenses 4916-1 and 4916-2. The Diopter switch 4906 may also comprisea directional switch that actuates a motor within the handheld unit body4903 to rotate gears within the handheld unit body 4903 based on thedirection in which the healthcare professional directs the switch toadjust the refractive power of the binocular OCT device 4900. As thebinocular OCT device 4900 may comprise an electronic device, thebinocular OCT device 4900 may comprise a power switch 4906 to controlpowering of the binocular OCT device 4900.

Each of the eyecups 4901-1 and 4901-2 can be threadedly mounted andcoupled to the housing to allow adjustment of the position of the eyeduring measurements. Work in relation to the present disclosure suggeststhat the eyecups can be adjusted by a healthcare professional and lockedin place to allow sufficiently reproducible positioning of the eye forretinal thickness measurements as described herein. Alternatively, or incombination, an eye position sensor, such as a Purkinje image sensor canbe used to determine a distance from the eye to the OCT measurementsystem.

The binocular OCT device 4900 may comprise appropriate dimensions andweight for in home measurements and for the user to take the binocularOCT system on trips. For example, the binocular OCT system may comprisea suitable length, a suitable width and a suitable height. The lengthcan extend along an axis corresponding to the users viewing direction.The length can be within a range from about 90 mm to about 150 mm, forexample about 130 mm. The width can extend laterally to the length andcan be within a range from about 90 mm to about 150 mm for example about130 mm. The height can be within a range from about 20 mm to about 50mm, for example. In some embodiments, the length is within a range fromabout 110 mm to 210 mm, the width within a range from about 100 mm to200 mm and a height within a range from about 50 mm to about 110 mm. Insome embodiments, a maximum distance across the device is within a rangefrom about 200 mm to about 350 mm, for example approximately 300 mm.

The weight of the binocular OCT system can be within a range from about1 pound to two pounds, e.g. 0.5 kg to about 1 kg.

The binocular OCT device 4900 can be configured to be dropped and stillfunction properly. For example, the binocular OCT device can beconfigured to be dropped from a height of about 30 cm and still functionso as to perform retinal thickness measurements accurately, e.g. with achange in measured retinal thickness of no more than the repeatabilityof the measurements. The binocular OCT system can be configured to bedropped from a height of about 1 meter without presenting a safetyhazard, for example from glass breaking.

FIG. 2B shows a block diagram of the binocular OCT device 4900illustrating various components within the handheld unit body 4903, inaccordance with some embodiments. For instance, the binocular OCT device4900 comprises free space optics 4910-1 and 4910-2. Each of the freespace optics 4910-1 and 4910-2 comprises a fixation target 4912 for itsrespective eye that allows the user to fixate/gaze on the target whilethe user's retinal thickness is being measured, and to allow fixationwith the other eye, so as to provide binocular fixation. The fixationtarget may comprise an aperture back illuminated with a light sourcesuch as an LED, (e.g., a circular aperture to form a disc shapedillumination target, although a cross or other suitable fixationstimulus may be used. The free space optics 4910-1 and 4910-2 may alsocomprise refractive error (RE) correction modules 4911-1 and 4911-2,respectively, that comprises the lenses 4916-1 and 4916-2, respectively.These lenses can be moved to preprogrammed positions corresponding tothe refractive error of the appropriate eye. A peripheral board 4915-1and 4915-2 in the free space optics modules 4910-1 and 4910-2 provideselectronic control over a motorized stage 4914-1 and 4914-2,respectively to correct for the refractive error of the respective eyeviewing the fixation target of the binocular OCT device 4900.

As discussed herein, the binocular OCT device 4900 may comprise eye cups4901-1 and 4901-2 that may be used to comfortably rest the binocular OCTdevice 4900 on the user's face. They may also be configured to block outexternal light as the user gazes into the binocular OCT device 4900. Theeye cups 4901 may also comprise eye cup adjustment mechanisms 4980-1 and4980-2 that allow the health care professional and optionally the userto move the eye cups 4901-1 and 4901-2 back and forth with respect tothe handheld unit body 4903 to comfortably position the eye cups on theuser's face and appropriately position each eye for measurement.

In some embodiments, the binocular OCT device 4900 comprises a fiberedinterferometer module 4950 that comprises a single VCSEL or a pluralityof VCSELs 4952. The one or more VCSELs 4952 are optically coupled to afiber distribution module 4953, which is optically coupled to fiberMach-Zehnder interferometer 4951. With embodiments comprising aplurality of VCSELs 4952, the VCSELS may each comprise a range ofwavelengths different from other VCSEL 4952 in the plurality in order toextend a spectral range of light. For example, each VCSEL 4952 may pulselaser light that is swept over a range of wavelengths for some durationof time. The swept range of each VCSEL 4952 may partially overlap anadjacent swept range of another VCSEL 4952 in the plurality as describedherein. Thus, the overall swept range of wavelengths of the plurality ofVCSELs 4952 may be extended to a larger wavelength sweep range.Additionally, the firing of the laser light from the plurality of VCSELs4952 may be sequential. For example, a first VCSEL of the plurality ofVCSELs 4952 may sweep a laser pulse over a first wavelength for someduration. Then, a second VCSEL of the plurality of VCSELs 4952 may sweepa laser pulse over a second wavelength for some similar duration, then athird, and so on.

The laser light from the VCSELs 4952 is optically transferred to thefiber distribution module 4953, where a portion of the laser light isoptically transferred to a fiber connector 4960 for analysis in a mainelectronic board 4970. The fiber connector 4960 may connect a pluralityof optical fibers from the fiber distribution module 4953 to the fiberconnector module 4960. Another portion of the laser light is opticallytransferred to an optical path distance correction (OPD) module 4940 andultimately to the free space retinal thickness optics 4910-1 fordelivery to a user's eye and measurement of the user's eye with aportion of the measurement arm of the Mach-Zehnder interferometer. Forexample, the OPD correction module 4940 may comprise a peripheral board4943 that is controlled by the main electronic board 4970 to actuate amotorized stage 4942 to change the optical path distance between theuser's eye, a coupler of the Mach-Zehnder interferometer and the one ormore VCSELs 4952. The OPD correction module 4940 may also comprise afiber collimator 4941 that collimates the laser light from the VCSELs4952 before delivery to the user's eye, and the fiber collimator can betranslated with the OPD correction module 4940.

A controller interface 4930 may be used to receive user inputs tocontrol the binocular OCT measurement system. The controller interfacemay comprise a first controller interface 4930-1 and a second controllerinterface 4930-2. The controller interface 4930 may comprise a triggerbutton mechanism that allows a user to initiate a sequence of steps toalign the eye and measure the retina as described herein. Alternatively,or in combination, the device may be configured with an auto-capturefunction, such that the data is automatically acquired when the deviceis aligned to the eye within appropriate tolerances.

Additionally, the binocular OCT device 4900 may comprise a scannermodule 4990 that scans the laser light from the one or more VCSELs 4952in a pattern (e.g., a stop and go scan pattern, a star scan pattern, acontinuous scan pattern, a Lissajous scan pattern, or a flower scanpattern (rose curve)). For example, a peripheral board 4991 of thescanner module 4990 may be communicatively coupled to the mainelectronic board 4970 to receive control signals that direct the scannermodule 4992 to scan the pulsed laser light from the VCSELs 4952 in apattern to perform an optical coherence tomography (OCT) on the user'seye. The scanning module 4990 may comprise a sealing window 4992 thatreceives the laser light from the fiber collimator 4941 and opticallytransfers the laser light to a free space two-dimensional scanner 4993,which provides the scan pattern of the laser light. The two-dimensionalscanner may comprise a scanner as described herein, such as a two-axisgalvanometer, or a two axis electro-static scanner, for example. Whenpresent, the sealing window 4992 may be used to keep the internalcomponents of the binocular OCT device 4900 free of dirt and/ormoisture. The laser light is then optically transferred to relay optics4994 such that the scanned laser light can be input to the user's eyevia the free space RT optics 4910-1. In this regard, the scanned laserlight may be transferred to a hot mirror 4913 such that infrared lightmay be reflected back towards the hot mirror, the scanning mirror andfocused into an optical fiber tip coupled to the collimation lens. Thehot mirror 4913 generally transmits visible light and reflects infraredlight, and may comprise a dichroic short pass mirror, for example.

The scanner and associated optics can be configured to scan any suitablysized region of the retina, such as regions comprising the fovea. Insome embodiments, the scanner is configured to scan the retina with ascanning pattern, such as a predetermined scanning pattern in responseto instructions stored on a processor such as the controller. Forexample, the scanner can be configured to scan the retina over an areacomprising a maximum distance across within a range from about 1.5 to 3mm, for example. The scanning region of the retina may comprise an arealarger than maps of retinal thickness in order to account for slighterrors in alignment, e.g. up to 0.5 mm in the lateral positioning of theeye in relation to the OCT system, for example in order to compensatefor alignment errors, e.g. by aligning the map based on the measuredposition of the eye. The size of the OCT measurement beam on the retinacan be within a range from about 25 microns to about 75 microns. In someembodiments, the mirror is moved with a continuous trajectorycorresponding to a scan rate on the retina within a range from about 10mm per second to about 200 mm per second, and the scan rate can bewithin a range from about 50 mm per second to about 200 mm per second.The displacement of the beam during an A-scan can be within a range fromabout 2 to 10 microns, for example. The beams for each of a plurality ofA-scans can overlap. In some embodiments, the mirror moves continuouslywith one or more rotations corresponding to the trajectory of the scanpattern and the swept source VCSEL turns on and off with a suitablefrequency in relation to the size of the beam and the velocity of thebeam on the retina. In some embodiments each of the plurality of A-scansoverlaps on the retina during at least a portion of the scan pattern.

In embodiments where the one or more VCSELs comprises a plurality ofVCSELs, the plurality of VCSELs can be sequentially scanned for eachA-scan, such that the measurement beams from each of the plurality ofVCSELs overlaps on the retina with a prior scan. For example, each ofthe sequentially generated beams from each of the plurality of VCSELsfrom a first A-scan can overlap with each of the sequentially generatedbeams from each of the plurality of VCSELs from a second A-scan alongthe trajectory.

As described herein, the binocular OCT device 4900 may comprise an IPDadjustment via the components 4905-1 and/or 4905-2. These components maybe communicatively coupled to a manual translation stage IP adjustmentmodule 4982 that perform the actuation of the free space optics modules4910-1 and 4910-2, so as to change a separation distance between thefree space optics modules and adjust the IPD.

The main electronic board 4970 may comprise a variety of components. Forexample, a photodetector 4972 may be used to receive laser lightdirected from the VCSELs 4952 through the fiber connector 4960 as wellinterfering light reflected from the user's eye. The fiber connector4960 may comprise a module 4961 that couples a plurality of opticalfibers, for example four optical fibers, to a plurality of detectors,for example five detectors. The fiber connector 4960 may also comprisean interferometer clock box 4962 (e.g. an etalon) that may be used inphase wrapping light reflected back from the user's eyes, as shown anddescribed herein. Once received by the photodetectors 4972, thephotodetectors 4972 may convert the light into electronic signals to beprocessed on the main electronic board 4970 and/or another processingdevice. The plurality of photo detectors may comprise two detectors of abalanced detector pair coupled to the fiber Mach-Zehnder interferometer,a clock box detector, and a pair of power measurement detectors, forexample.

The main electronic board 4970 may comprise a communication power module4973 (e.g., a Universal Serial Bus, or “USB”) that can communicativelycouple the binocular OCT device 4900 to another processing system,provide power to the binocular OCT device 4900, and/or charge a batteryof the binoculars OCT device 4900. Of course, the binocular OCT device4900 may comprise other modules that may be used to communicateinformation from the binocular OCT device 4900 to another device,including for example, Wi-Fi, Bluetooth, ethernet, FireWire, etc.

The main electronic board 4970 may also comprise VCSEL drivingelectronics 4971 which direct how and when the VCSELs 4952 are to befired towards the user's eyes. Other components on the main electronicboard 4970 comprise an analog block 4974 and a digital block 4975 whichmay be used to process and/or generate analog and digital signals,respectively, being transmitted to the binocular OCT device 4900 (e.g.,from an external processing system), being received from variouscomponents within the binocular OCT device 4900, and/or being receivedfrom various components within the binocular OCT device 4900. Forexample, the peripheral feedback button 4932 may generate an analogsignal that is processed by the analog block 4974 and/or digital clock4975, which may in turn generate a control signal that is used tostimulate the motorized stage module 4942 via the peripheral board 4943.Alternatively, or additionally, the analog block 4974 may process analogsignals from the photodetectors 4972 such that they may be converted todigital signals by the digital block 4975 for subsequent digital signalprocessing (e.g., FFTs, phase wrapping analysis, etc.).

FIG. 2C shows a schematic of an optical configuration 5100 that may beimplemented with the OCT binocular 4900, in accordance with someembodiments. The optical configuration 5100 comprises one or more VCSELs4952 that are fiber coupled via an optical coupler 5126. As discussedabove, the one or more VCSELs 4952 may be swept over a range ofwavelengths when fired. For embodiments with a plurality of VCSELs 4952,the wavelengths may partially overlap a wavelength sweep range ofanother VCSEL 4952 in the plurality so as to increase in overall sweeprange of the VCSELs 4952. In some instances, this overall sweep range iscentered around approximately 850 nm. The laser light from the one ormore VCSELs 4952 is propagated through the fiber coupler 5126 to a fiberoptic line 5127, where another optical coupler 5118 splits a portion ofthe optical energy from the one or more VCSELs 4952 along two differentpaths.

In the first path, approximately 95% of the optical energy is opticallytransferred to another optical coupler 5119 with approximately 5% of theoptical energy being optically transferred to an optical coupler 5120.In the second path, the optical energy is split yet again via an opticalcoupler 5120. In this regard, approximately 75% of the optical energyfrom the optical coupler 5120 is transferred to a phase correctiondetector 5101-1 through an interferometer such as a Fabry Perotinterferometer comprising an etalon. The etalon and detector maycomprise components of an optical clock 5125. The optical clock 5125 maycomprise a single etalon, for example. The etalon may comprisesubstantially parallel flat surfaces and be tilted with respect to apropagation direction of the laser beam. The surfaces may comprisecoated or uncoated surfaces. The material may comprise any suitablelight transmissive material with a suitable thickness. For example, theetalon may comprise a thickness within a range from about 0.25 mm toabout 5 mm, for example within a range from about 0.5 mm to about 4 mm.The reflectance of the etalon surfaces can be within a range from about3% to about 10%. The etalon can be tilted with respect to the laser beampropagation direction, for example tilted at an angle within a rangefrom about 5 degrees to about 12 degrees. The finesse of the etalon canbe within a range from about 0.5 to about 2.0, for example, for examplewithin a range from about 0.5 to 1.0. The etalon may comprise anysuitable material such as an optical glass. The thickness, index ofrefraction, reflectance and tilt angle of the etalon can be configuredto provide a substantially sinusoidal optical signal at the clock boxdetector. The finesse within the range from about 0.5 to 2.0 can providesubstantially sinusoidal detector signals that are well suited for phasecompensation as described herein, although embodiments with higherfinesse values can be effectively utilized.

In some embodiments, the clockbox may comprise a plurality of etalons.The approach can be helpful in embodiments wherein the one or moreVCSELs comprises a plurality of VCSELs, and the plurality of etalonsprovides additional phase and clock signal information. For example, theclockbox may comprise a first etalon and a second etalon arranged sothat light is transmitted sequentially through the first etalon and thenthe second etalon, e.g. a series configuration, which can providefrequency mixing of the clock box signals and decrease the number ofdetectors and associated circuitry used to measure phase of the sweptsource. Alternatively, the plurality of etalons can be arranged in aparallel configuration with a plurality of etalons coupled to aplurality of detectors.

The phase correction detector 5101-1 may use the light signals from theoptical clock 5125 to correct the phase of light reflected from a user'seyes 5109-1 by matching the phases of the one or VCSELs 4952 via phasewrapping of the light from the one or more VCSELs 4952 as describedherein. The remaining 25% of the optical energy from the optical coupler5120 may be optically transferred to a detector 5101-2 for opticalsafety. For instance, the detector 5101-2 may be used to determine howmuch optical energy is being transferred to the user's eye 5109-1 or5109-2, depending on the orientation of the device. If the binocular OCTdevice 4900 determines that the detector 5101-2 is receiving too muchoptical energy that may damage the user's eyes, then the binocular OCTdevice 4900 may operate as a “kill switch” that shuts down the VCSELs4952. Alternatively, or additionally, the binocular OCT device 4900 maymonitor the detector 5101-2 to increase or decrease the optical energyfrom the VCSELs 4952 as deemed necessary for laser safety and/or signalprocessing. The OCT device may comprise a second safety detector 5101-3to provide a redundant measurement for improved eye safety.

The optical energy transferred to the optical coupler 5119 (e.g.,approximately 95% of the optical energy from the one or more VCSELs4952) is also split along two paths with approximately 99% of theremaining optical energy being optically transferred along a fiber to anoptical coupling element 5122 and with approximately 1% of the remainingoptical energy also being optically transferred to a detector 5101-3 forlaser safety of the binocular OCT device 4900. The portion of theoptical energy transferred to the to the optical coupler 5122 may besplit by the optical coupler 5122 between two optical path loops 5110and 5111 of the Mach-Zehnder interferometer, approximately 50% each, forexample. The optical path loop 5110 may comprise a reference arm of theinterferometer and provide a reference optical signal for the retinalthickness measurement of the user's eye 5109-1 (e.g., the measurementsignal reflected from the user's retina through the optical path loop5111).

The portion of the optical energy transferred through the optical loop5111 is transferred to the user's left eye 5109-1 along the measurementarm of the Mach-Zehnder interferometer. For instance, the optical energybeing transferred to the user's eye 5109-1 may pass through the OPDcorrection module 4940 to perform any optical path distance correctionsappropriate to the interferometer of the binocular OCT device 4900. Thislight may then be scanned across the user's eye 5109-1 via a scanningmirror 5113 of the scanner module 4990 to measure the retinal thicknessof the user's eye 5109-1 while the user's eye 5109-1 is fixated on afixation target 4912-1 (e.g., along a fixation path 5106-1).

The fixation target 4912-1 can be back illuminated with LED 5102-1, andlight may be propagated along the optical path 5106-1 through opticalelements 5103-1 and 5105-1 and the dichroic mirror 5115, comprising ahot mirror. In some instances, the target of fixation may also includean illumination stop 5104 so as to provide relief to the user's eye5109-1 while fixating on the target.

The light impinging the user's retina of the eye 5109-1 may be reflectedback along the path established by the OPD correction module 4940, thescanning mirror 5113, the focusing element 5114, the dichroic mirror5115, and the optical element 4916-1, through the optical loop 5111, andback to the optical coupler 5122. In this instance, the optical coupler5122 may optically transfer the reflected optical energy to an opticalcoupler 5121 which may couple the reflected optical energy with theoptical energy that was split into the optical loop 5110. The opticalcoupler 5121 may then optically transfer that optical energy to thebalanced detector's 5101-4 and 5101-5 such that a retinal thicknessmeasurement can be performed. In doing so, the optical coupler 5121 maysplit that optical energy to approximately 50% to each of the detectors5101-1 and 5101-4, such that the interference signals arrive out ofphase on the balanced detectors.

The light may be focused through a plurality of optical elements 5112and 5114, being directed to the user's eye 5109-1 via a dichroic mirror5115 and focused on the user's retina via the optical element 4916-1.The light from the scanning mirror 5113 and the light reflected from theuser's eye 5109 are both shown as reflecting off the dichroic mirror5115, which may comprise hot mirror 4913 configured to generally reflectinfrared light and transmit visible light.

As can be seen in this example, the user's right eye 5109-2 does notreceive any optical energy from the one or more VCSELs 4972 with theorientation shown. Rather, the user's right eye 5109-2 is used forbinocular fixation with the target 4912-2, which can be back illuminatedwith another LED 5102-2. The target 4912-2 can be of similar size andshape to target 4912-1 and be presented to the eye with similar optics,so as to provide binuclear fixation. In this regard, the user's righteye 5109-2 may also fixate on the target 4912-2 along an optical path5106-2 through the optical elements 4916-2, 5105-2, 5103-2, and theillumination stop 5104-2, which comprises similar optical power,separation distances and dimensions to the optics along optical path5106-1.

The binocular OCT system 4900 can be configured to move opticalcomponents to a customized configuration for the user being measured.Lens 4916-1 can be adjusted along optical path 5106-1 in accordance withthe refraction, e.g. eyeglass prescription of the eye being measured.Lens 4916-1 can be moved under computer, user or other control to adjustlens 4916-1 to bring the fixation target 4912-1 into focus and to focusthe measurement beam of the OCT interferometer on the user's retina. Forexample, the lens can be translated as shown with arrow 5146. Lens4916-2 can be moved under computer, user or other control to adjust lens4916-2 to bring the fixation target 4912-2 into focus on the user'sretina. For example, the lens can be translated as shown with arrow5144. The OPD correction module 4940 can be translated axially towardand away from mirror 5113 as shown with arrows 5146. The OPD correctionmodule 4940 can be moved under computer control to appropriatelyposition the optical path difference between the measurement arm and thereference arm for the user's eye being measured. The interpupillarydistance can be adjusted by translating the optical path 5106-2 towardand away from optical path 5106-1.

The free space optics module 4910-2 may comprise one or more componentsalong optical path 5106-2, such as the LED 5101-2, the fixation target4912-2, lens 5103-2, aperture 5104-2, lens 5105-2, or lens 4916-2. Thefree space optics module 4910-2 can be translated laterally toward andaway from the optical components located along optical path 5106-1 toadjust the inter pupillary distance as shown with arrow 5142. The freespace retinal thickness optics module 4910-1 may comprise one or morecomponents located along optical path 5106-1, such as the LED 5102-1,the fixation target 5103-1, the aperture 5104-1, the mirror 5116, thelens 5105-1, the mirror 5115, or lens 4916-1. The OPD correction module5146 may comprise the optical fiber of the measurement arm of theinterferometer, and lens 5112 to substantially collimate light from theoptical fiber and to focus light from the retina into the optical fiber.

In some embodiments, an A-scan represents a depth reflectivity profileof a sample and may result from performing a Fourier Transform on adetected interferogram that is obtained while varying the wavelength ofthe light source such as a VCSEL, as described herein. In someembodiments, a B-scan comprises a 2D image corresponding to a slice oftissue along a plane. In some embodiments, a B-scan image is generatedby scanning the measurement beam along a sample in a linear scanpattern, where the B-scan comprises a plurality of A-scans along thescan pattern. In some embodiments, each of the plurality of A-scans usedto form the B-scan represents interferometric data collected at ameasurement location or point along the scan pattern. Alternatively, orin combination, a B-scan can be generated from a non-linear scan patternso as to represent a slice of tissue along a linear section of tissue,for example with one or more of interpolation or mapping of thenon-linear scan pattern as described herein.

As described, an OCT system operates to move a measurement beam of lighton a retina in a specific scan pattern. This scan pattern may takeseveral different forms, including but not limited to a stop and go scanpattern, a star scan pattern, a continuous scan pattern, a linear scanpattern, a Lissajous scan pattern, or a flower scan pattern. FIG. 3shows an example of a scan pattern (termed a “flower” scan patternherein) that may be used to collect OCT data, in accordance with someembodiments. The scan pattern 300 shown in the figure is also referredto as a rose curve, where a rose curve is a polar coordinaterepresentation of a sinusoid. The flower scan pattern 300 comprises aplurality of lobes 310 or petals, with one end of each lobe beingconnected to and extending radially outward from a central point orlocation 320. The flower pattern shown in the figure has 12 lobes orpetals, although a different number may be present in a scan pattern.

The figure shows a superposition of the scan pattern on a patient's eyeand indicates several regions of tissue of the eye, such as the retinaltissue. The three concentric rings or annular regions 330 (shown bydashed lines) in the figure represent different zones or regions of aretina of a patient's eye. In some embodiments, the innermost ring 332represents at least a portion of the fovea region of a patient's eye,the middle ring 334 represents the macular region of a patient's eye,and the outermost ring 336 represents a region outside the fovea. Thesector or region in between the innermost ring 332 and the middle ring334 is divided into 4 zones in the figure. Similarly, the sector orregion in between the middle ring 334 and the outermost ring 336 isdivided into 4 zones in the figure. In some embodiments, the pluralityof zones comprises a total of 9 identified zones or regions of apatient's retina. In some embodiments, the innermost ring has a diameterof about 1 mm and contains the fovea, which may have a diameter of about0.35 mm. In some embodiments, the middle ring has a diameter of about 2mm and contains the macula, which may have a diameter of about 1.5 mm.In some embodiments, the outermost ring has a diameter of about 2.5 mmand represents the retinal region outside the macula.

In the example scan pattern shown in FIG. 3 , each dot along the scantrajectory represents a location on the retina at which a measurement ismade and data is collected. In some embodiments, this may result fromturning on a light source to generate a measurement beam at those pointsalong the pattern and turning off the light source at other points alongthe pattern. Note that the density of measurements (i.e., the spacingbetween the measurement points or dots) varies along different regionsor sections of the trajectory.

As shown in the example, the density of measurements is less for theportion of a lobe that lies within the innermost ring 332. The densityof measurement points increases for the portion of the scan pattern thatlies outside the innermost ring 332, increasing for the portion betweenrings 332 and 334, and further increasing for the portion at the end ortip of a lobe, which in the example, lies outside the middle ring 334.Thus, in this example, the density of measurement and data collectionpoints varies along the scan.

In some embodiments, the density of measurement points along a scanpattern may be controlled by varying the scan speed of the scanningmirror and the geometry of the scan pattern generated by the scanningmirror, while maintaining the same A-Scan acquisition rate. Note thateach lobe 310 comprises a substantially continuous scan pattern with anunscanned region inside the lobe or scan path of the measurement beam.As indicated by the measurement points and the variation in density ofthose points, the measurement beam and/or the sampling of data is notcontinuous and is instead modulated (turned on and off) during thescanning process.

The scanning mirror may be caused to move by applying a voltage orcurrent waveform to one or more actuators, such as amicroelectromechanical (MEMs) device. In some embodiments, the mirrormay be caused to move by application of an electrostatic force. Theelectrostatic force may be provided by one or more capacitors. In someembodiments, the position or orientation of the mirror may be caused tomove by application of an electromagnetic force. In some embodiments,the electromagnetic force may be provided by one or more of agalvanometer, an electrostatic transducer, or a piezo electrictransducer.

During operation of the OCT system, a drive signal or waveform (orwaveforms) is input to a scanner or scanning mirror controller. Thedrive signal operates to cause an actuator or actuators to move themirror. This may be accomplished by causing the mirror to rotate aboutthe X and/or Y-axes. As the mirror is moved, a measurement beam thatreflects off the mirror is redirected and caused to move on a patient'sretina in accordance with a scan pattern that is determined by the inputdrive signal or signals. The light reflected from the surface orinternal layers of the retina interferes with a reference version of themeasurement beam to form an interferogram which is detected by adetector. Thus, a drive signal to one or more actuators may be varied tocause a measurement beam to be scanned on a retina in a desired scanpattern, with the data detected and stored by other elements of an OCTsystem.

FIG. 4 shows a set of interferograms or A-scans 400 acquired by an OCTusing the scan pattern or trajectory of FIG. 3 , in accordance with someembodiments. In the figure, a set of A-scans have been stacked on top ofeach other in to generate the image shown. In some embodiments, eachA-scan is generated by measuring an intensity of an interferogram as theone or more VCSELs is swept in wavelength over time, and Fouriertransforming the measured interferogram. Thus, in FIG. 4 , a set ofFourier transformed interferograms is shown, in which each Fouriertransformed interferogram corresponds to an A-scan. Each A-scan of themeasurement beam along the scan pattern generates one horizontal row ofpixels in the figure. An OCT system is able to image different depths ofthe retina and its associated tissue structures by varying the positionof a reference mirror. For example, the figure shows an image of theinner limiting membrane (ILM) 410 and the Retinal Pigment Epithelium(RPE) 420 obtained by concatenating or stacking multiple scans performedduring a cycle of the scan pattern of FIG. 3 .

In some embodiments, the data collected using one scan pattern may besubjected to further processing to obtain data that would be expected tobe generated by a second scan pattern. In some embodiments, this mayinvolve interpolating, extrapolating or otherwise processing measurementdata acquired as a result of the scan pattern of FIG. 3 to produce datathat would be expected to be acquired as a result of a second anddifferent scan pattern.

As an example, FIG. 5 shows the scan pattern of FIG. 3 superimposed on aradial scan pattern, data for which may be obtained by interpolation ofthe data obtained from the scan pattern of FIG. 3 , in accordance withsome embodiments. In this example, data obtained by movement of ameasurement beam along a flower scan pattern 510 may be interpolated orotherwise processed to produce the data expected by performing a scanover the “star” or radial pattern 520. The interpolation, extrapolationor other form of processing used to generate data corresponding to adifferent scan pattern may be based on any suitable technique ormethodology, including but not limited to linear interpolation,polynomial interpolation, nearest neighbor interpolation, or splineinterpolation, among others.

Although FIG. 5 illustrates a star or radial scan pattern, it should beunderstood that interpolation, extrapolation or other processing ofmeasurement data obtained by use of a flower or rose curve scan patternmay be used to generate measurement data corresponding to other types ofscan patterns, including but not limited to stop and go, circular, star,Lissajous, linear, raster and other patterns. In some embodiments, thisallows data acquired using a flower, curved, or lobed scan pattern to beused to “simulate” or represent data that would be obtained using aradial, linear, or other scan pattern.

FIG. 6 shows how the surface of a patient's eye may be divided intozones or regions for purposes of comparing scan patterns by comparingthe amount of scanning or scan time spent collecting data from eachzone, in accordance with some embodiments. As shown in the figure, asurface of an eye may be divided into a set of zones, in this case 9zones. Each zone is identified by a label Z0, Z1 to Z8 in the figure. Insome embodiments, each of the zones can be used to generate a retinalthickness map, in which the overall thickness, e.g. average thickness,for each zone is shown. In some embodiments, data from measurements ofthe same eye at different times are compared to generate a map showingchanges in retinal thickness for each of the zones over time.

As has been described, measurements of retinal thickness and changes inretinal thickness over time can provide indications of disease orillness, even for diseases or illnesses not directly related to the eye.This is one reason for the value of obtaining OCT scan data andprocessing that data to enable it to be used to create images that canbe analyzed to determine retinal thickness.

Although some OCT systems enable the collection and processing of OCTscan data to enhance images showing the ILM and RPE layers of theretina, interpretation of those images can still be difficult and proneto error. The fuzziness or lack of distinct boundaries between thelayers can introduce uncertainty into measurements of retinal thickness.One way of reducing these inaccuracies is by training a machine learningmodel to “segment” the images into better defined ILM and RPE layers.This segmentation enables a more accurate measurement of retinalthickness, and as mentioned, this information is helpful in thediagnosis and treatment of eye diseases. In some embodiments, thesegmentation of OCT images is performed using a trained neural network.

As described herein, a trained convolutional neural network (CNN) can beused to segment an interferogram and provide a resulting image that canbe used more effectively to determine a measurement of retinalthickness. In some examples, this is the result of the CNN operating onan image to enhance the boundaries of an inner limiting membrane (ILM)layer, where the ILM is the boundary between the retina and the vitreousbody of the eye. Using a CNN or other form of trained image processingmodel assists in identifying the boundaries of the tissue layers in theretina and obtaining more accurate measurements of retinal thickness.

However, as mentioned, training a CNN or other form of neural networkrequires a relatively large set of properly annotated training data.Unfortunately, a sufficiently large set of annotated data may not beavailable for interferograms produced by a specific type of OCT deviceor system, such as one that operates using a different scan pattern thanthat used to generate scans for which more data is available. Forexample, at present there is a relatively large amount of data availablefor scans generated using a radial or raster scan pattern, butrelatively little for scans generated using other forms of scanpatterns. This can make it difficult to train and use a CNN to segmentimages generated from scans that result from using a scan pattern thatis not a radial pattern.

FIG. 7 shows a process 700 for training a CNN or other form of neuralnetwork to perform a segmentation of an interferogram image, inaccordance with some embodiments. As shown in the figure, in someembodiments, the training data comprises OCT scan data from two sources:(1) a first source 710 (referred to as “Reference B-Scan” in the figure,and associated annotations or labels 712 (referred to as “Annotation” inthe figure); and (2) a second source 720 (referred to as PBOSInterferogram” in the figure, and associated annotations or labels 722(referred to as “Annotation” in the figure).

More generally, the two sources represent a first source of dataobtained from operating an OCT system and based on moving a measurementbeam in a first scan pattern and a second source obtained from operatingan OCT system (which is typically a different OCT system, but is notrequired to be) and based on moving a measurement beam in a second anddifferent scan pattern. In some embodiments, the first scan pattern is alinear (for example, radial) scan pattern and the second scan pattern isa curved (for example, flower) scan pattern.

In some embodiments, the amount of information, data, scans, images, orinterferograms available from one of the two sources may be sufficientfor purposes of training a CNN, while the other is relatively less andconsidered insufficient for training purposes. In some embodiments, theimages or interferograms obtained from one of the OCT systems or scanpatterns may be higher resolution than those obtained from the other OCTsystem or scan pattern.

In some embodiments, the trained neural network may be intended toprocess images or interferograms obtained using a scan pattern for whichthere is not sufficient training data. In some embodiments, this may bethe type of scan referred to as a PBOS Interferogram 720 in the figure.In some embodiments, scan 720 may be based on data obtained using acurved scan pattern. As a result, if it is desired to be able to performimage segmentation or another form of image processing on an imageformed using data obtained from a curved scan pattern, then a processfor training a neural network that can utilize images obtained from adifferent scan pattern, for example a linear scan pattern is desired.FIG. 7 illustrates an example of such a training process.

In some embodiments, images generated from data obtained using bothtypes of scan patterns (a linear and a curved scan pattern) are used aspart of the training process. As will be described in greater detail,one or both of the sources of training data may be subject to additionalprocessing prior to being used for training the CNN. Further, in someembodiments, the image input to the trained CNN (in this example a PBOSinterferogram 720) may be subjected to further processing prior to beinginput to the trained CNN.

Because the two sources of OCT data (710 and 720) represent dataobtained from systems that use different scan patterns and/or havediffering resolution, the scans, interferograms, or images obtain fromone type of scan pattern may benefit from further processing prior tobeing used to train a CNN or being used as input to a trained CNN. Thisfurther processing may rely on the same or different forms of imageprocessing (e.g., translating, sampling, flipping, blurring,interpolating, etc.).

In some embodiments, the further processing is used to generate a largerset of scan data or images for use as training data for a CNN. Theadditional training data is formed from B-scan images 710 based on datagenerated using the first type of scan pattern. As mentioned, in oneexample, the trained CNN performs image processing on images generatedfrom the scan pattern data or images obtained using the second scanpattern (720).

In some embodiments, the scan pattern data or images obtained using thesecond scan pattern may be subjected to further processing prior tobeing used for purposes of training or as input to the trained model. Insome embodiments, this further processing may comprise interpolating orextrapolating data obtained using the second scan pattern to producedata that would be expected to result from using the first scan pattern.In some embodiments, this comprises interpolating data obtained using acurved scan pattern to produce data that would be expected to resultfrom using a linear scan pattern.

In some embodiments, the further processing may be used to alter imagesformed from data obtained using the first scan pattern so that theimages more closely resemble images formed from data obtained using thesecond scan pattern. In some embodiments, this type of processing may beused to process an original set of training data prior to its input to aCNN. In some embodiments, this type of processing may be used togenerate additional training data after application of other processesto generate variations of an original set of images.

As shown in the figure, in some embodiments, annotated scan imagesobtained from a first scan pattern 710 may be subjected to alteration byapplication of an image alteration ruleset 730 prior to being used astraining data for a Neural Network 740. In some embodiments, NeuralNetwork 740 may comprise a CNN and have a specific architecture,referred to as a U-Net. As described in greater detail with reference toFIG. 12 , a U-Net neural network consists of a contracting path and anexpanding path, which results in the u-shaped architecture. Thecontracting path is a convolutional network that consists of repeatedapplication of convolutions, each followed by a rectified linear unit(ReLU) and a max pooling operation. During the contraction stages, thespatial information is reduced while feature information is increased.The expanding path combines the feature and spatial information througha sequence of up-convolutions and concatenations with high-resolutionfeatures from the contracting path.

Image alteration ruleset 730 may comprise a set of image processingoperations that are applied to data or images obtained from scans 710 toenable that data or images to be used as inputs to train Neural Network740. In some embodiments, the trained network is then used to process orsegment data or images obtained from scans 720. In some embodiments,image alteration ruleset 730 may comprise one or more image processingoperations such as non-linear subsampling, scaling, flipping,translation, brightness and contrast adaptation, or application of aGaussian blur filter.

As mentioned, and as shown in FIG. 7 , in some embodiments, scan data orimages based on the second type of scan pattern 720 may also be used inthe training process. In such cases, those images are annotated 722 anddata or images based on both types of scan patterns are used as trainingdata. Further, in some embodiments, data or images based on the firsttype of scan pattern may be processed as described to generateadditional training data. Still further, in some embodiments, data basedon the second type of scan pattern may be interpolated, extrapolated, orotherwise processed to generate training data. In some embodiments,annotations 722 may be derived by interpolating annotations 712; thismay be useful when an interferogram 720 is not easily or reliablyannotated by a human because of the relatively low optical resolution ofthe measurement data. In these cases, interpolation of annotations orlabels may be required as the scan pattern used to generate scans 710and 720 are not identical.

When trained, the input data or image to the trained neural network 740may be data or an image based on the second type of scan pattern, eitherin its original form or after being interpolated, extrapolated, orotherwise processed. In some embodiments, the interpolation or otherdata processing may be to generate data or an image that more closelyresembles that which would be obtained from the first scan pattern. Insome embodiments, this interpolation may operate on data obtained from acurved scan pattern to generate data that would be expected to beobtained from a linear scan pattern.

FIG. 8 shows a set of operations that may be used in a process forgenerating additional training data for use in training a CNN or otherform of neural network as described with reference to FIG. 7 , inaccordance with some embodiments. As shown in the figure, an originalimage 810 (such as one obtained from segmenting a B-scan based on afirst scan pattern 710) may be subjected to operations that includerandom horizontal flipping (as suggested by the image shown in 820),random shifting in the x direction (830), random scaling along the yaxis (840), random translation in the y direction (850), Gaussianblurring (860), or a variable elastic transformation (870). Thesynthetic oversampling of the original images produces slightly alteredtraining images and its use as additional training data may minimize therisk of overfitting of the model to the training set. In someembodiments, these types of geometric transforms may be referred to astechniques for augmenting a data set.

FIG. 9 shows an original B-scan based on a radial scan pattern, a resultof applying an image degradation ruleset to that scan pattern togenerate an interferogram, and an interferogram obtained by use of asecond scan pattern, in accordance with some embodiments. The figureshows an original image 910 based on a radial scan pattern (termed aB-scan in the figure). An image degradation or alteration ruleset 920 isapplied to image 910. As shown in the figure, application of the imagedegradation or alteration ruleset 920 to image 910 produces image 930(termed a “pseudo interferogram” in the figure). Note that applicationof image degradation or alteration ruleset 920 to image 910 generates animage 930 that more closely resembles that obtained from an OCT deviceperforming a second type of scan pattern 940 (in this case the flowerscan pattern of FIG. 3 ). In some embodiments, these types of dataprocessing operations may be referred to as techniques for degeneratingan image that is part of a data set.

This resemblance is a basis for an embodiment in which a trained neuralnetwork operates to generate a B-scan from an input scan obtained usinga different scan pattern than conventionally used to generate a B-scan.For example, given a set of training data comprising B-scan imagesobtained from a linear scan pattern and a second set of images obtainedfrom a curved scan pattern, a CNN may be trained to associate annotatedfeatures in the B-scan images with the corresponding features in thesecond set of images. In some cases, the B-scan images may be subjectedto one or more of the processing operations shown and described withreference to FIGS. 8 and 9 .

In some cases, the data obtained from the curved scan pattern may beinterpolated or otherwise processed to more closely correspond to thedata obtained for a specific linear scan pattern or region of a retinascanned using a linear scan pattern prior to being used as trainingdata. In some embodiments, this is referred to as a resampling processor operation. When trained, the neural network may operate to receive asan input an image obtained using the curved scan pattern (or aninterpolated set of data generated from the curved scan pattern) and inresponse output an image corresponding to a B-scan that would beobtained for a specific linear scan pattern performed at a region of aretina.

This embodiment allows use of a curved scan pattern to generate datausing a first OCT device to be the source of an image that wouldconventionally be generated by use of a linear scan pattern performed bya second OCT device. Similarly, it allows use of data generated by anOCT system that executes a first scan pattern to be used as part oftraining a model to segment data generated by an OCT system thatexecutes a second scan pattern.

FIG. 10A shows an original interferogram 1010 and a segmentedinterferogram 1020 obtained from processing the original interferogramusing a trained CNN, in accordance with some embodiments. Originalinterferogram 1010 (identified as “resulting interferogram” in thefigure) is constructed from multiple scans using the scan pattern ofFIG. 3 that capture data obtained from different depths into a retina.One or more A-scans (which may be averaged or subject to other signalprocessing to combine the scan data from multiple scans) at a locationon the scan pattern of FIG. 3 produces data corresponding to a singlevertical line in the figure. Data from a plurality of scans are combinedto produce the interferogram 1010 shown. When this type of interferogramis input to a trained neural network of the type described withreference to FIGS. 7 and 12 , the output is a segmented interferogramimage 1020. Segmented interferogram image 1020 more readily identifiescertain tissue layers or layer boundaries, such as the ILM and RPElayers. This can improve the ability to determine changes in retinalthickness over time and the identification of fluid or fluid pools inthe retina. Interferogram 1022 is another example of the output that maybe generated by a trained CNN in response to the input of interferogram1010. In some embodiments, the output may consist of other or additionalsegmentation classes, e.g., one or more of intra-retinal fluid (“IRF”),subretinal fluid (“SRF”), pigment epithelium detachment (“PED”), etc.

Note that in some embodiments, processing of interferogram 1010 toimprove the segmentation may be performed, such as that termed decurvingand described in U.S. Provisional Patent Application 62/706,417, titled“System and Method for Optical Coherence Tomography A-Scan Decurving”,filed Aug. 14, 2020, the entire contents of which is incorporated byreference.

FIG. 10B shows an example of the flower pattern scan pattern of FIG. 3that was used to obtain the interferogram of FIG. 10A, including anindication of the portion of the scan pattern that generated theindicated section of the interferogram (shown by arrow 1030 in each ofFIGS. 10A and 10B).

FIG. 11A is a flow chart or flow diagram 1100 illustrating a process,method, operation, or function for training a neural network using a setof OCT interferograms obtained using a first scan pattern to determine aretinal tissue structure in a set of OCT interferograms obtained using asecond scan pattern, in accordance with some embodiments. The steps orstages shown in the figure may be performed in whole or in part as aresult of the execution of a set of instructions by a programmedprocessor or processing unit. As shown in the figure, at step 1110, afirst plurality of interferograms are obtained. These interferograms arebased on data collected by an OCT system using a first scan pattern, forexample a radial scan pattern. At step 1120, each of the first pluralityof interferograms are annotated or labeled to indicate one or moretissue structures of a retina. These tissue structures may include anILM or RPE, for example. Typically, the annotation or labeling isperformed by a human who has expertise in the subject matter of theinterferograms. In some examples, the annotation or labeling may beperformed with the assistance of a rule-set and image processingsoftware, or another type of automated or semi-automated process.

In some embodiments, the annotation may be assigned to each pixel in animage and may comprise one of Background, Foreground, IntraretinalFluid, Subretinal Fluid, or Pigment Epithelium Detachment.

At step 1130, a neural network is trained using the first plurality ofinterferograms and the associated annotations. In some embodiments, theneural network may be a CNN, and more specifically a U-Net architecture,described in greater detail with reference to FIG. 12 . At step 1140, asecond plurality of interferograms are input to the trained neuralnetwork. The second plurality of interferograms are based on datacollected by an OCT system using a second and different scan pattern,for example the flower scan pattern of FIG. 3 . At step 1150, the outputof the trained neural network is obtained, where the output indicatesthe tissue structure of the retina scanned using the second scanpattern.

The embodiment of FIG. 11A represents one of several task or objectivesthat may be performed by a suitably trained model. In the exampleembodiment of FIG. 11A, a model is trained using training data (and theassociated annotations) obtained from a first OCT system that operatesto acquire image data using a first scan pattern. After training, themodel accepts as an input data generated by a second OCT system thatoperates to acquire data using a second scan pattern and segments thatimage data.

However, as mentioned, in some cases, there may not be sufficienttraining data available or the available training data may need to besupplemented or altered to enable it to be used to train a model tooperate on input data obtained from the second OCT system. FIG. 11B is aflow chart or flow diagram 1102 illustrating a process, method,operation, or function for generating additional training data fortraining a neural network using a set of OCT interferograms obtainedusing a first OCT system to determine a retinal tissue structure in aset of OCT interferograms obtained using a second OCT system, inaccordance with some embodiments.

As shown in FIG. 11B, in some embodiments, at step 1112, a firstplurality of interferograms are obtained by using a first OCT system toscan a retina or retinas. The first OCT system may have an associatedresolution, scan pattern or other characteristic. Each interferogram isthen annotated or labeled, which typically involves mapping each pixelto a class, such as a structure, layer, boundary, or feature of aretina, as suggested by step 1122. Next, at step 1124, new or additionaltraining data is generated. As described herein, the new or additionaltraining data may be used with the first plurality of interferograms andannotations described with reference to steps 1112 and 1122, or insteadof those interferograms and annotations as a replacement set of trainingdata and associated annotations.

Next, at step 1132, a model is produced (such as a trained neuralnetwork) using one or more of the first plurality of interferograms, theoriginal annotations, the new training data (and associatedannotations), or the additional training data (and associatedannotations). The new or additional training data and annotations may begenerated by one or more of the following data processing techniques:(1) Augmentation—this set of techniques or operations is used togenerate additional training data by applying one or more operations(geometrical transformations, such as those illustrated in FIG. 8 ) to aset of data associated with an image. Augmentation is used to provideincreased data variability, increase the robustness of the trainedmodel, and prevent over-fitting of the model to the original set ofdata. The geometrical transformations may be applied to thecorresponding annotations to generate annotations for the image dataproduced by the augmentation process; (2) Degeneration—this set oftechniques or operations (such as blurring) is applied to original imagedata obtained from an OCT system with a higher resolution to obtain datathat would be expected to be obtained from an OCT system with lowerresolution. In some embodiments, degenerated images may be used as partof a curriculum learning process for training a model; (3)Resampling—this technique or operation is applied to image data obtainedusing a first scan pattern to generate image data expected to beobtained using a second and different scan pattern (typically from adifferent OCT system). Resampling operations may comprisenearest-neighbor interpolation, extrapolation, curve-fitting, etc.; and(4) Registering or registration—this technique or operation is used toalign annotations or indications of features (boundaries, regions,fluid, etc.) made to a first set of images to those in a second set ofOCT images obtained by degenerating the first set of images.

After being trained, the neural network (or other form of trained model)receives as an input a second plurality of interferograms obtained fromusing a second OCT system, as suggested by step 1142. At a step 1152,output is obtained of the trained neural network indicating tissuestructure of the retina interferograms obtained from the second OCsystem. The output of the trained model is a segmentation of the inputimages/interferograms indicating a structure, layer, boundary, feature,pool of liquid, or other aspect of an image. The segmented image may beobtained by mapping each pixel to a class or classes.

FIG. 11C is a diagram illustrating an embodiment 1160 in which imagedata obtained from a first OCT system and its associated annotations aresubjected to one or more of resampling, degeneration, and augmentationoperations to generate additional training data for use in training amodel that is being trained with image data obtained from a second OCTsystem and its associated annotations. As shown in the figure, imagedata obtained from a first OCT system 1162 and its associatedannotations 1163 are both subjected to a resampling process. Theresampling process may involve interpolation, extrapolation, curvefitting, or other suitable data processing technique. The result of theresampling process is a set of resampled image data obtained from thefirst OCT system 1164 and a set of associated resampled annotations1165. Next, the resampled image data 1164 is subjected to one or more ofdegeneration or augmentation, as suggested by step 1166. Degenerationmay involve blurring or other transforms or processes that operate oninitial image data to produce image data corresponding to an image oflower resolution. Augmentation may involve geometrical transforms oroperations such as those described with reference to FIG. 8 . The resultis image data obtained from a first OCT system that is made to besimilar to that expected to be obtained from a second OCT system, wherethe second OCT system is of lower resolution and operates using adifferent scan pattern than the first OCT system. In addition, by use ofthe augmentation operations additional training data has been generatedto assist in preventing over-fitting of the model to the original set ofdata. As also shown in the figure, the resampled annotation data 1165 issubjected to an augmentation process to generate additional annotationdata 1167 that may be used with the resampled, degenerated, or augmenteddata 1166 as part of a training process.

The processing described with reference to FIG. 11C generates additionaltraining data and annotations that may be used with image data obtainedfrom a second OCT system 1168 and its associated annotations 1169 totrain a model. The trained model is used to segment image data obtainedusing the second OCT system. However, because insufficient training data(or annotations for data) is available for the second OCT system (whichwould typically have a different resolution and utilize a different scanpattern than the first OCT system), one or more of the resampling,degeneration, and augmentation techniques are applied to image data (andin some cases, to the annotations) obtained from the first OCT device togenerate additional training data and annotations to be used with theavailable training data and annotations.

The annotations 1169 for the image data obtained from the second OCTsystem may be obtained directly by human or machine annotation of theimage data 1168 for the second OCT system, or by one or more ofaugmentation, resampling, or registering of the annotations 1163 for theimage data 1162 obtained from the first OCT system. The registering orregistration of annotations or labels may depend on the characteristicsof the first or of the second device and may comprise consideration oftilt or shift between scans, scan pattern, scan location, or othercharacteristics.

As described, original image data and the associated annotations may beused alone or with additional training data and annotations to train amodel used to segment image data. In some embodiments, the additionaltraining data may be generated by one or more of augmentation,degeneration, or resampling operations or processes. Similarly, theannotations associated with the additional training data may be based onaugmentation, resampling, or registration of the annotations associatedwith the original image data.

In some embodiments, a process of transfer learning or curriculumlearning may be used as part of training a model used for segmentationof image data. Herein transfer learning refers to a process whereby amodel (or layers that are part of a model) that has been trained for onetask or objective is used for a different one. This may involveinserting one or more hidden layers of a previously trained model into anew model and then completing the training of the new model using a setof training data.

Curriculum learning refers to a process whereby a model is trained byprogressively increasing the difficulty of the task with each iterationof a training cycle. As an example, this may be achieved byprogressively decreasing the quality of training data (e.g., bydegeneration) for each successive iteration or set of iterations,thereby increasing the difficulty in correctly classifying the data. Insome embodiments, this may be accomplished by degenerating higherresolution image data to a greater degree as the number of iterationsincreases and/or adding lower resolution image data obtained from adifferent OCT system into the training data set with a higherprobability as the number of iterations increases.

In this regard, the processing illustrated in FIG. 11C may also orinstead be used as part of a curriculum learning process for training amodel as it results in generating lower quality image and annotationdata which may be used by itself or with the original image andannotation data for training.

FIG. 11D is a set of diagrams 1170 illustrating an embodiment in whichtraining data obtained from an open access data set of interferograms(retinal images) 1172 is subjected to augmentation and degenerationprocesses 1173 to generate training data for a model that is intended tobe used with input data obtained from an OCT system having a lowerresolution than the OCT system used to generate the interferograms 1172.The initial data 1172 is annotated to produce annotated or labeledimages 1174 that indicate the classes corresponding to pixels in theimages. As suggested by 1174, the annotations may identify severalclasses or features of the original images, including features such astissue layers, tissue boundaries, pools of fluid, or other structures ina retina. The annotated images 1174 may be simplified by removal ofcertain class identifications to produce a simplified set of annotations1175 for use in training the model. In some embodiments, the simplifiedannotations 1175 result from applying one or more of the geometrictransforms applied to image data 1172 as part of the data augmentationprocess. Degenerated and augmented image data 1173 and correspondingannotations 1175 may then be used as training data and labels for amodel. When trained, the model 1176 (depicted as a neural network, andmore specifically, a U-Net architecture) operates to generate asegmented image 1177 from input image data that corresponds to an OCTsystem having a lower resolution than that used to generate the originalimage data 1172.

FIG. 12 is a diagram illustrating an example of a convolutional neuralnetwork (CNN) architecture that may be used to process an interferogramimage 1210 and the output of the CNN representing a segmented image1220, in accordance with some embodiments. As shown in the figure, theCNN includes a contractor path 1230 that operates to exchange spatialfeatures of an image for semantic features followed by an expansion path1240 that operates to exchange the semantic features for spatialfeatures.

The contractor path 1230 may be thought of as an encoder that typicallyincludes a pre-trained classification network applying convolutionblocks 1232 followed by a maxpool down-sampling. The result is to encodean input image into feature representations at multiple differentlevels. The expansion path 1240 may be thought of as a decoder thatsemantically projects the discriminative features (i.e., lowerresolution features) learnt by the encoder onto the pixel space (therebyproducing a higher resolution) to provide a dense classification.Typically, the decoder consists of up-sampling and concatenationoperations followed by convolution operations. Up-sampling may bereferred to as transposed convolution, up-convolution, or deconvolution,and up-sampling methods include Nearest Neighbor, BilinearInterpolation, and Transposed Convolution, for example.

Each convolution operation 1232 is typically implemented as a point-wisemultiplication operation (such as a dot-product between an image sectionand a weighting value) followed by a summing operation, with severalweighting or filter layers (referred to as kernels) being applied insome examples. In one example embodiment, a U-Net architecture for a CNNthat may be used to process image data (such as that shown in FIG. 12 )comprises 18 convolutional layers, 1.79 million biases and weights, andbetween 32 and 256 semantic feature channels.

After each stage of image processing the result is concatenated orcombined with the data created at the previous processing stage to formthe final set of processed image data. As an example, after training theCNN of FIG. 12 may operate on an image of the type shown (in thisexample, an image formed from data collected by operating an OCT systemin a curved scan pattern) to generate a segmented output image. Theoutput image may then be used to better identify tissue structures inthe retina and to make more reliable measurements of retinal thickness.

As mentioned, an example of a convolutional neural network architecturethat may be used to implement one or more of the trained modelsdescribed is referred to as a U-Net architecture. In particular, theUNet 3+ architecture has been found to be beneficial as it combines deepsupervision during training with the skipping of connections betweencertain separated hidden layers. This enables the fusing of high-levelsemantic information with high-resolution spatial information. The UNet3+ architecture is described in an article entitled “Unet 3+: AFull-Scale Connected Unet For Medical Image Segmentation”(arxiv.org/ftp/arxiv/papers/2004/2004.08790.pdf). In some embodiments,the convolutional neural network has between 5 and 19 hidden layers andan activation layer comprised of a ReLU (rectified linear unit).

FIG. 13 is a diagram illustrating how a set of scan data obtained usingthe flower scan pattern of FIG. 3 may be subjected to further dataprocessing operations (such as resampling involving interpolation orgaussian blurring) to generate an image representing a B-scan of aselected cross section of a retina, in accordance with some embodiments.As shown in the figure, data collected using the flower scan pattern ofFIG. 3 (1310) may be used to generate an image representing a B-scan1320, with the image generated being determined by user selection of aspecific cross section of interest on the flower scan data pattern ofdata points (as represented by line 1312).

As will be described further with reference to FIGS. 14 and 15 , in someembodiments, a user may select a desired cross-sectional “slice” of dataobtained using a flower scan pattern (as an example) and in response,the systems and methods described herein may generate a correspondingB-scan image. Depending upon the selected cross-section, the flowerpattern scan data may be resampled by interpolation or another processto generate data that would typically result from a linear radial scanor raster scan, with the generated data being used as part of formingthe B-scan. As a result, the output image represents a B-scan that wouldresult from a linear scan of a specific type, although the original datawas obtained using a flower scan pattern.

FIG. 14 is a diagram illustrating further examples of B-scans generatedby processing of data obtained using the flower scan pattern of FIG. 3for different slices through the pattern to create B-scans of differentcross sections of a retina that would be obtained from a raster scan, inaccordance with some embodiments. As shown in the figure, by selecting ahorizontal line through the data generated using the flower scanpattern, a B-scan image corresponding to a raster scan at a specificregion of a retina may be generated.

For example, a slice 1410 through the flower scan pattern would generatea scan of the type shown in 1420. Similarly, a slice 1430 through theflower scan pattern would generate a scan of the type shown in 1440. Aslice 1450 through the flower scan pattern would generate a scan of thetype shown in 1460. A slice 1470 through the flower scan pattern wouldgenerate a scan of the type shown in 1480.

FIG. 15 is a diagram illustrating further examples of B-scans generatedby processing of data obtained using the flower scan pattern of FIG. 3for different slices through the pattern to create B-scans of differentcross sections of a retina that would be obtained from a radial scan, inaccordance with some embodiments. As shown in the figure, by selecting adiagonal line including the origin through the data generated using theflower scan pattern, a B-scan image corresponding to a radial scan at aspecific region of a retina may be generated.

For example, a slice 1510 through the flower scan pattern would generatea scan of the type shown in 1520. Similarly, a slice 1530 through theflower scan pattern would generate a scan of the type shown in 1540. Aslice 1550 through the flower scan pattern would generate a scan of thetype shown in 1560. A slice 1570 through the flower scan pattern wouldgenerate a scan of the type shown in 1580.

FIG. 16 is a diagram illustrating how a set of the created B-scans ofdifferent cross sections of a retina may be combined to produce a 3Dvisualization or thickness map of a retina 1620, in accordance with someembodiments. The figure illustrates how images generated from differentsections of data obtained using a scan pattern may be combined toproduce volumetric data that can be visualized over the scan pattern.Note that the thickness map is not limited to the 9 regions or zonesdescribed with respect to FIG. 6 . This 3D volumetric data may alsoinclude internal structures such as fluid pooling.

FIG. 17A is a diagram illustrating a comparison of the performance of aconventional scan pattern and data processing method to the resultsobtained using the flower scan pattern and image processing using thetrained CNN described herein, in accordance with some embodiments. Graph1710 shows the variation or scatter in data obtained using a Lissajousscan pattern (in this example) and a Gaussian Mixture Model (GMM) datafitting approach. As indicated on graph 1710, the R² value for the “fit”to the regression model is a value of 0.459, suggesting a relativelylarge amount of variation in the data.

Graph 1720 shows the variation or scatter in data obtained using aflower scan pattern (with 12 petals or lobes, in this example) and atrained neural network of the type described herein for processing theimage. As indicated on graph 1720, the R² value for the “fit” to theregression line is a value of 0.965, suggesting a relatively smalleramount of variation in the data and a better fit to the regressionmodel. This suggests that the trained neural network is capable ofgenerating more consistent results.

FIG. 17B is a diagram illustrating a curriculum training process inwhich image data and/or annotations obtained from a first OCT deviceconfiguration with higher resolution and a second OCT deviceconfiguration with lower resolution are used for training over a set oftraining iterations, with some of that data subjected to degeneration.The image quality and image degeneration of the training images areshown in relation to the training iterations. In some embodiments,increasing degeneration of the training images corresponds to decreasingimage quality. Image data 1770 comprises a plurality of images from a2^(nd) OCT device configuration, which includes a substantially fixeddecreased image quality, e.g. lower resolution, increased distortion,noise, etc., such as a personalized biometry system (PBOS) as describedherein. Three types of data are shown as follows: 1) data 1770 from thesecond OCT device configuration; 2) data 1760 from a first OCT deviceconfiguration with high resolution, which has been resampled, thendegenerated, in which the degeneration is randomized and progressivelyincreases over time; and 3) data 1750 from the first OCT deviceconfiguration with high resolution, which is resampled and thenprogressively degenerated with a linear increase in degeneration. Insome embodiments, the pixel resolution of the training images remainssubstantially fixed while the degeneration increases and correspondingimage quality decreases. For example, the substantially fixed pixelresolution of the resampled images may correspond to the resolution ofthe 2^(nd) OCT device configuration, such that the resampled anddegenerated images from the first OCT device configuration can becombined with the images from the 2^(nd) OCT device configuration, forexample interspersed among each other for the iterations of the trainingprocess.

The relative quality of the image data is also shown, which includes atarget image quality 1765 (shown with a line) and a resampled imagequality 1755 (shown with a line). The resampled image quality 1755corresponds to the image quality of the resampled images from the firstOCT device configuration with high resolution that have been downsampled to a lower lateral resolution and have not yet been degenerated.In some embodiments, resampling comprises lateral down sampling toprovide a reduction of lateral resolution, and degeneration comprisesaxial down sampling to provide a reduction of axial resolution. Thetarget image quality 1765 is chosen to correspond to the image qualityof the image data 1770 from the second OCT device configuration withlower resolution, such that the degenerated image data mimics the imagedata 1770. For example, the quality of image data 1760 and image data1750 converge toward the target image quality 1765 near the end 1780 ofthe training session 1780. This approach allows the trained network toreceive input images from the second low resolution device and to outputannotated images such as segmented images as described herein.

The quality of the image data 1770 from the lower resolution system isused to establish a target image quality 1765 for the training of theneural network. With progressive training iterations of the neuralnetwork, the quality of the degenerated input training images for theneural network converges toward target image quality 1765. This can beachieved by degenerating the resampled images. The resampled images maycomprise image quality 1755. These resampled images can be degeneratedby an appropriate amount and used as input to the training model. Theamount of degeneration can be related to the number of trainingiterations. For example, resampled and linearly degenerated data 1750 isshown along a line 1785 which corresponds to linearly increasing imagedegeneration and linearly decreasing image quality. In some embodiments,the image quality is within a variable range 1790 that extends from thetarget amount 1765 to a lower threshold amount of degeneration shown atline 1785. The amount of degeneration can be increased with progressiveiterations to provide input training images with an image quality thatapproximates image quality 1765 that is consistent with the image data1770. As shown with arrow 1775, the image quality of the images 1770corresponds to image quality 1765, such that the image quality of thedegenerated images near the end of the training process substantiallycorresponds to the image quality of the lower resolution OCT system.

As shown in FIG. 17B, as the training iterations increase (moving to theright on the x-axis), the quality of the image data being used fortraining decreases (as indicated by the upward arrow along the y-axis).In some embodiments, the resolution of the data may be decreasing as thenumber of iterations of the training process increases. Alternatively,the resolution may be substantially fixed during the training process soas to correspond to the resolution of the second OCT deviceconfiguration with lower resolution. The decrease in data quality (e.g.resolution) may be the result of one or more of resampling data (e.g.data obtained from a OCT device with lower resolution), degeneratingdata obtained from a OCT device with higher resolution (e.g. where thedegree of degeneration may be randomized and may increase as the numberof iterations increases), or progressively degenerating data obtainedfrom a OCT device with higher resolution as the number of iterationsincreases. The decrease in data quality with an increase in trainingiterations corresponds to an increase in task difficulty.

As discussed with reference to FIG. 11C, in some embodiments usingcurriculum learning, higher resolution image data may be degenerated toa greater degree as the number of iterations increases and/or lowerresolution image data obtained from a different OCT system may be addedto the training data set with a higher probability as the number ofiterations increases.

In some embodiments, a combination of transfer learning and curriculumlearning may be used in a training process. In such embodiments, a setof training images may be formed from two sources: images generated by ahigher resolution OCT system configuration and images generated by alower resolution OCT system configuration. The combination of images inthe set of training images provides a source of transfer learning asinputs to a training process as described herein. The images generatedby the higher resolution OCT system may be degenerated in one or more ofmany ways as described herein, e.g. resampled to a lower resolution anddistorted, to better resemble the resolution and other properties of theimages generated by the lower resolution OCT system. In order togenerate the degenerated images, the images from the higher resolutionOCT system may be subjected to a linear or randomly increasing amount ofdegeneration with each successive training iteration. In someembodiments, images from the OCT system with higher resolution areresampled, e.g. down sampled, to provide a lower resolutioncorresponding to the lower resolution OCT system configuration, and thenfurther degenerated by an amount to correspond to the image quality ofthe lower resolution OCT system. The amount of degeneration may comprisea linearly increasing degeneration corresponding to a progressivelyincreasing difficulty, or a randomly increasing degeneration with arandomly increasing difficulty of at least a threshold amount. In someembodiments, the overall set of training images is formed from acombination of the images generated by the lower resolution OCT systemand the images formed by linear or randomly increasing the amount ofdegeneration of the resampled images generated by the higher resolutionOCT system.

While the curriculum transfer learning can be configured in many ways,in some embodiments a level of difficulty for the next image in thetraining data set is determined, and an appropriate amount ofdegeneration is applied to the resampled OCT image from the higherresolution OCT image in order to provide the next image to the trainingdataset. The amount of degeneration may comprise a linearly increasingamount, or a random amount with a progressively increasing minimumthreshold amount of degeneration, so that the degree of degeneration andcorresponding difficulty generally increase toward image quality 1765.Referring again to FIG. 17B, image quality can be determined withreference to the image quality 1755 of the resampled high-resolutionimages and the image quality 1765 of corresponding to the low resolutionOCT device. The resampled images with image quality 1755 can bedegenerated by an appropriate amount to correspond to an appropriateamount of difficulty for a particular iteration. In some embodiments,the level of difficulty can be determined with a linearly increasingdegree of difficulty, for example with reference to image data 1750,which has a linearly increasing amount of degeneration and linearlyincreasing difficulty, in which the high-resolution image data from thefirst OCT device configuration is resampled to correspond to imagequality 1750 and then degenerated by an appropriate amount to correspondto the decreased image data quality shown. Alternatively or incombination, the degree of difficulty for a particular iteration maycomprise a random amount of difficulty within a range 1790 extendingfrom target image quality 1765 to a linearly increasing threshold amountshown with line 1785. For both the linearly increasing difficulty andthe randomly increasing difficulty, as the number of iterationsincrease, the image quality approaches image quality 1765. Once theimage quality, e.g. learning difficulty, for a training image has beendetermined, the resampled image can be degenerated by an appropriateamount to correspond to the determined image data quality and/orlearning difficulty for the next training image in the dataset.

The approaches describe herein can be configured and combined in manyways, in accordance with the present disclosure. In some embodiments, aninitial training data set comprises a plurality of images from alow-resolution OCT system and a plurality of resampled images from thehigher resolution OCT system. The artificial intelligence model such asa neural network is trained with this initial data set. Once thetraining has been completed with the initial training data set, themodel can then be trained with a resampled and degenerated images. Theresampled and degenerated images may comprise a combination of imageswith a randomly selected difficulty (for example with reference to imagedata 1760) and a linearly increasing difficulty (for example withreference to image data 1750), both derived from a higher resolution OCTsystem configuration. In some embodiments, the resampled and degeneratedimages of increasing difficulty are combined with the lower resolutionimages (for example with reference to image data 1770) from the lowerresolution system configuration.

In some embodiments, the training data set comprises a combination ofimages from a second low resolution OCT device configuration anddegenerated images from a first higher resolution OCT deviceconfiguration. In some embodiments, the training data set comprises acombination of image data from a second low resolution OCT deviceconfiguration, e.g. image data 1770, resampled and linearly degeneratedimage data from a first higher resolution OCT device configuration, e.g.image data 1750, and resampled and randomly degenerated image data froma first higher resolution OCT device, e.g. image data 1760. In someembodiments, the pixel resolution of the training image data remainssubstantially fixed at the pixel resolution of the second lowerresolution OCT device configuration. The image data input into the modelmay comprise segmented and grey level images subjected to degenerationand augmentation as described herein, for example.

Embodiments of the disclosed techniques and methods for generatingtraining data and training a model (such as a neural network) to segmentimage data obtained from an OCT system comprise use of multiplecombinations of image data and associated annotations, where one or moreoperations or processes may be applied to the image data, annotations,or both. In some embodiments, annotations associated with image datafrom a first OCT device may be resampled and registered to provideannotations for image data from a second OCT device, where the seconddevice has a different scan pattern than the first device. The imagedata and annotations for the second device may then be used to train amodel. If desired, the image data from the first device may also be usedas training data after resampling.

In another embodiment, image data and associated annotations from afirst device may be subjected to degeneration to generate training dataand associated annotations corresponding to a second device having alower resolution than the first device. The degenerated data andannotations may be subjected to one or more of resampling oraugmentation to generate additional training data and annotations. Theannotations may be registered to image data from the second device. Theadditional training data and/or image data and annotations for thesecond device may then be used to train a model.

In another embodiment, image data and associated annotations from afirst device may be used as part of a transfer learning technique togenerate training data and annotations for training a model to processdata from a second device. In this embodiment, data from the seconddevice is not used.

In another embodiment and example of a transfer learning process, imagedata from a first device is resampled, degenerated, and augmented withthe associated annotations being resampled and augmented to generatetraining data and annotations for a model to process data from a seconddevice. In this embodiment, image data and annotations from the seconddevice are not used.

In another embodiment, image data and associated annotations for a firstOCT device may be used as part of a transfer learning process with imagedata and associated annotations from a second OCT device to train amodel to process data from the second device.

In another embodiment, image data from a first device is resampled,degenerated, and augmented with the associated annotations beingresampled and augmented to generate training data and annotations for amodel to process data from a second device. In this embodiment, imagedata and associated annotations from the second OCT device may be usedas part of the training data for a model. In this or other embodiments,the annotations for the image data from the second device may beobtained directly from the image data for the second device or through aresampling and registering of annotations for image data from the firstdevice.

The OCT systems, data processing methods and devices described hereinmay be operated or implemented in accordance with a variety ofparameters, settings, programmed configurations, etc. The exampleoperating parameters or characteristics, or range of parameters providedherein are intended to provide guidance to practicing the system anddevice (or to implementing the process or methods described) and are notmeant to provide limits on operational characteristics. As will beapparent to one of skill, other combinations or values of operatingparameters or characteristics are possible and are included within thedescription provided in this disclosure.

As an example, in some embodiments, the scan pattern is a flower patternor rose curve and has a plurality of lobes. In some embodiments, thenumber of lobes may vary between four (4) and twenty-four (24). In someembodiments, a scan may be repeated by the device between two (2) andtwenty (20) times to collect data.

In some embodiments, a measurement beam path of the scan pattern for asingle scan extends a distance within a range from 10 mm to 100 mm, andoptionally from 12 mm to 60 mm. In some embodiments, a total measurementbeam path of the scan pattern repeated for the plurality of timesextends a total distance within a range from 100 mm to 1000 mm, andoptionally from 120 mm to 600 mm. In some embodiments, a total time ofthe scan pattern repeated the plurality of times is within a range from1 to 3 seconds, and optionally within a range from 1.5 seconds to 2.5seconds. In some embodiments, the scanner comprises one or moreactuators for altering a position of the mirror to move the measurementbeam on the retina. In some embodiments, a velocity of the measurementbeam moving along the trajectory during a scan is within a range from 10mm/s to 400 mm/s, and optionally from 15 mm/s to 300 mm/s. In someembodiments, a processor is configured with instructions to generate aplurality of A-scans of the retina with each A-scan comprising thescanner moving the measurement beam along each of the plurality of lobesof a scan pattern, and wherein a sampling rate of the A-scans is withina range from 10 kHz to 50 kHz, and optionally within a range from 15 kHzto 25 kHz.

As used herein, the terms “OCT device” and “OCT system” are usedinterchangeably.

As described herein, the computing devices and systems described and/orillustrated herein broadly represent any type or form of computingdevice or system capable of executing computer-readable instructions,such as those contained within the modules described herein. In theirmost basic configuration, these computing device(s) may each comprise atleast one memory device and at least one physical processor.

The term “memory” or “memory device,” as used herein, generallyrepresents any type or form of volatile or non-volatile storage deviceor medium capable of storing data and/or computer-readable instructions.In one example, a memory device may store, load, and/or maintain one ormore of the modules described herein. Examples of memory devicescomprise, without limitation, Random Access Memory (RAM), Read OnlyMemory (ROM), flash memory, Hard Disk Drives (HDDs), Solid-State Drives(SSDs), optical disk drives, caches, variations or combinations of oneor more of the same, or any other suitable storage memory.

In addition, the term “processor” or “physical processor,” as usedherein, generally refers to any type or form of hardware-implementedprocessing unit capable of interpreting and/or executingcomputer-readable instructions. In one example, a physical processor mayaccess and/or modify one or more modules stored in the above-describedmemory device. Examples of physical processors comprise, withoutlimitation, microprocessors, microcontrollers, Central Processing Units(CPUs), Field-Programmable Gate Arrays (FPGAs) that implement softcoreprocessors, Application-Specific Integrated Circuits (ASICs), portionsof one or more of the same, variations or combinations of one or more ofthe same, or any other suitable physical processor. The processor maycomprise a distributed processor system, e.g. running parallelprocessors, or a remote processor such as a server, and combinationsthereof.

Although illustrated as separate elements, the method steps describedand/or illustrated herein may represent portions of a singleapplication. In addition, in some embodiments one or more of these stepsmay represent or correspond to one or more software applications orprograms that, when executed by a computing device, may cause thecomputing device to perform one or more tasks, such as the method step.

In addition, one or more of the devices described herein may transformdata, physical devices, and/or representations of physical devices fromone form to another. Additionally or alternatively, one or more of themodules recited herein may transform a processor, volatile memory,non-volatile memory, and/or any other portion of a physical computingdevice from one form of computing device to another form of computingdevice by executing on the computing device, storing data on thecomputing device, and/or otherwise interacting with the computingdevice.

The term “computer-readable medium,” as used herein, generally refers toany form of device, carrier, or medium capable of storing or carryingcomputer-readable instructions. Examples of computer-readable mediacomprise, without limitation, transmission-type media, such as carrierwaves, and non-transitory-type media, such as magnetic-storage media(e.g., hard disk drives, tape drives, and floppy disks), optical-storagemedia (e.g., Compact Disks (CDs), Digital Video Disks (DVDs), andBLU-RAY disks), electronic-storage media (e.g., solid-state drives andflash media), and other distribution systems.

A person of ordinary skill in the art will recognize that any process ormethod disclosed herein can be modified in many ways. The processparameters and sequence of the steps described and/or illustrated hereinare given by way of example only and can be varied as desired. Forexample, while the steps illustrated and/or described herein may beshown or discussed in a particular order, these steps do not necessarilyneed to be performed in the order illustrated or discussed.

The various exemplary methods described and/or illustrated herein mayalso omit one or more of the steps described or illustrated herein orcomprise additional steps in addition to those disclosed. Further, astep of any method as disclosed herein can be combined with any one ormore steps of any other method as disclosed herein.

The processor as described herein can be configured to perform one ormore steps of any method disclosed herein. Alternatively, or incombination, the processor can be configured to combine one or moresteps of one or more methods as disclosed herein.

Unless otherwise noted, the terms “connected to” and “coupled to” (andtheir derivatives), as used in the specification and claims, are to beconstrued as permitting both direct and indirect (i.e., via otherelements or components) connection. In addition, the terms “a” or “an,”as used in the specification and claims, are to be construed as meaning“at least one of.” Finally, for ease of use, the terms “including” and“having” (and their derivatives), as used in the specification andclaims, are interchangeable with and shall have the same meaning as theword “comprising.

The processor as disclosed herein can be configured with instructions toperform any one or more steps of any method as disclosed herein.

It will be understood that although the terms “first,” “second,”“third”, etc. may be used herein to describe various layers, elements,components, regions or sections without referring to any particularorder or sequence of events. These terms are merely used to distinguishone layer, element, component, region or section from another layer,element, component, region or section. A first layer, element,component, region or section as described herein could be referred to asa second layer, element, component, region or section without departingfrom the teachings of the present disclosure.

As used herein, the term “or” is used inclusively to refer items in thealternative and in combination.

As used herein, characters such as numerals refer to like elements.

The present disclosure includes the following numbered clauses.

Clause 1. A method of processing data obtained from an OCT system, themethod comprising: obtaining a first plurality of images, wherein eachof the first plurality of images corresponds to data acquired by an OCTsystem performing a scan of a retina; annotating a plurality of pixelsfrom each of the first plurality of images to generate segmented imagedata of the retina, wherein the annotation identifies one or morestructures of the retina; generating a plurality of degenerated imagesfrom the first plurality of images by degenerating the first pluralityof images; and training a neural network using the plurality ofdegenerated images and the segmented image data.

Clause 2. The method of clause 1, wherein annotating comprises assigninga classification for each pixel of the plurality of pixels from saideach of the first plurality of images and optionally wherein saidclassification comprises an integer.

Clause 3. The method of clause 1, wherein the segmented image datacomprises a plurality of segmented images, each of the plurality ofsegmented images comprising an annotation defining a class for eachpixel of said each of the plurality of images.

Clause 4. The method of clause 1, wherein each of the plurality ofsegmented images corresponds to one of the plurality of degeneratedimages and wherein the plurality of segmented images and correspondingdegenerated images are input to the neural network to train the model.

Clause 5. The method of clause 3, wherein the plurality of segmentedimages comprises a first plurality of segmented images corresponding tothe first plurality of images and a second plurality of segmented imagescorresponding to the plurality of degenerated images.

Clause 6. The method of clause 1, wherein generating the plurality ofdegenerated images comprises applying a transform function to the firstplurality of images to cause a geometric transformation of the firstplurality of images.

Clause 7. The method of clause 5, wherein generating the plurality ofdegenerated images comprises applying a transform function to the firstplurality of images to cause a geometric transformation of the firstplurality of images, and wherein the transform function is applied tothe first plurality of segmented images to obtain the second pluralityof segmented images.

Clause 8. The method of clause 5, wherein each of the first plurality ofsegmented images comprises annotations at first locations for each of afirst plurality of pixels of the first plurality of segmented images andwherein each of the second plurality of segmented images comprises theannotations at second locations for each of a second plurality of pixelsof the second plurality of segmented images.

Clause 9. The method of clause 1, wherein, the one or more structures ofthe retina comprise background, retinal nerve fiber layer, ganglion celllayer and inner plexiform layer, outer plexiform layer and inner nuclearlayer, outer nuclear layer and external limiting membrane, retinalpigment epithelium and photoreceptors, chorio-capillaries andchorio-septae, and optionally wherein the annotation comprises one ormore of background, retina, intraretinal fluid, subretinal fluid, orretinal pigment epithelium detachment.

Clause 10. The method of clause 1, wherein, the first plurality ofimages is degenerated with one or more of resampling, down sampling,speckle noise, Y-Gaussian blur or A-Scan Y-jitter to generate thedegenerated images.

Clause 11. The method of clause 1, wherein the plurality of degeneratedimages comprises augmented images.

Clause 12. The method of clause 11, wherein, the augmented images aregenerated by applying one or more of curving, horizontal flip, X-roll,Y-scale, Y-translate, elastic transformation or Gamma contrast to thefirst plurality of images.

Clause 13. The method of clause 11, wherein the augmented images aregenerated by applying a geometric transform to the first plurality ofimages.

Clause 14. The method of clause 13, wherein the geometric transformcomprises one or more of curving, horizontal flip, X-roll, Y-scale,Y-translate, or elastic transformation.

Clause 15. The method of clause 5, further comprising: generating afirst plurality of geometrically transformed segmented images byapplying a geometric transform function to the first plurality ofsegmented images; and generating a second plurality of geometricallytransformed segmented images by applying the geometric transformfunction to the second plurality of segmented images.

Clause 16. The method of clause 1, wherein the OCT system comprises afirst configuration and wherein the plurality of degenerated images andsegmented image data comprise a transfer learning data set configured totrain the neural network to classify data from a second OCT system, thesecond OCT system comprising a second configuration different from thefirst configuration of the OCT system and optionally wherein the firstconfiguration differs from the second configuration by one or more of anaxial resolution, a scan pattern, or a lateral resolution.

Clause 17. The method of clause 16, wherein the transfer learningdataset comprises degenerated images and augmented images, the augmentedimages generated by applying one or more of curving, horizontal flip,X-roll, Y-scale, Y-translate, elastic transformation or Gamma contrastto the first plurality of images, and wherein the neural network isiteratively trained with a plurality of progressively increasinglydegenerated images generated from the first plurality of images andwherein an amount of degeneration progressively approaches one or moreof an axial resolution, a scan pattern, or a lateral resolution ofimages from the second configuration of the second OCT system.

Clause 18. The method of clause 1, wherein the first plurality of imagescorresponds to a first resolution of the OCT system and wherein theplurality of degenerated images corresponds to images of a second OCTsystem having a second resolution, wherein the first resolution isassociated with a smaller resolvable feature size than the secondresolution.

Clause 19. The method of clause 1, wherein the first plurality of imagesis annotated to define a ground truth data set for training the neuralnetwork and wherein the first plurality of images is resampled andregistered with a second plurality of images from a second OCT system.

Clause 20. The method of clause 1, wherein the OCT system comprises afirst OCT system, the first OCT system comprising a first configuration,and wherein the neural network, after training, is used to classify datafrom a second OCT system, the second OCT system comprising a secondconfiguration different from the first configuration, and optionallywherein the first configuration differs from the second configurationwith regards to one or more of an axial resolution, a scan pattern, or alateral resolution.

Clause 21. The method of clause 20, wherein the neural network is nottrained with data from the second OCT system.

Clause 22. The method of clause 20, wherein the first configuration ofthe OCT system comprises a first resolution and the second configurationof the second OCT system comprises a second resolution, and wherein thefirst resolution is associated with a smaller resolvable feature sizethan the second resolution.

Clause 23. The method of clause 20, wherein the neural network istrained with a transfer learning dataset, the transfer learning data setcomprising first degenerated and augmented OCT images from the first OCTsystem and corresponding annotated OCT images from the first OCT system.

Clause 24. The method of clause 23, wherein the transfer learningdataset comprises second OCT images from the second OCT system andcorresponding annotated OCT images from the second OCT system.

Clause 25. The method of clause 23, wherein the transfer learningdataset is derived from 1) resampled and annotated OCT image data fromthe first OCT system, 2) resampled, degenerated, and augmented OCT imagedata from the first OCT system; and 3) OCT image data and annotationdata from the second OCT system.

Clause 26. The method of clause 23, wherein the transfer learningdataset comprises OCT data from a plurality of eyes and wherein each ofthe plurality of eyes is measured with the first OCT system and with thesecond OCT system.

Clause 27. The method of clause 1, wherein a difficulty of a nextdegenerated image is determined from resampled image data, and the nextdegenerated image is generated in response to the difficulty, theresampled image data generated by resampling the first plurality ofimages.

Clause 28. The method of clause 1, wherein the plurality of degeneratedimages comprises a plurality of images of an increasing difficulty.

Clause 29. The method of clause 28, wherein the increasing difficultycomprises a linearly increasing difficulty.

Clause 30. The method of clause 28, the increasing difficulty comprisesa random difficulty above an increasing threshold of difficulty.

Clause 31. The method of clause 28, wherein the increasing difficultyincreases toward a difficulty of images from a second OCT system, thesecond OCT system comprising a lower resolution than the OCT system.

Clause 32. The method of clause 28, wherein the increasing difficultycomprises a combination of a linearly increasing difficulty and arandomly increasing difficulty.

Clause 33. A method of generating a segmented OCT image, comprising:receiving an OCT image, the OCT image comprising an axial resolution anda first plurality of pixels, wherein each of the first plurality ofpixels is associated with a corresponding grey level; processing thereceived OCT image with a trained model to generate the segmented OCTimage comprising a second plurality of pixels, wherein each of thesecond plurality of pixels is assigned to a class by the trained model,wherein the class comprises one of background, retina, intraretinalfluid, subretinal fluid, or retinal pigment epithelium detachment; andoutputting the segmented OCT image.

Clause 34. The method of clause 33, wherein the retina class comprisesone or more pools of intraretinal fluid not visible in the received OCTimage and wherein the one or more pools of intraretinal fluids isvisible in the segmented OCT image.

Clause 35. The method of clause 33, wherein the trained model comprisesa neural network and each of the plurality of pixels is assigned to theclass in response to a probability function of the neural network.

Clause 36. The method of clause 33, wherein the trained model comprisesa trained machine learning model that generates a neural network.

Clause 37. The method of clause 33, wherein the trained model comprisesa neural network and the neural network has been trained with aplurality of OCT images having a resolution associated with a smallerresolvable feature size than the axial resolution of the OCT image.

Clause 38. A method of processing data obtained from an OCT system, themethod comprising: obtaining a first plurality of images, wherein eachof the first plurality of images corresponds to data acquired by a firstOCT system performing a first plurality of scans of a plurality ofretinas with a first scan pattern; annotating a first plurality ofpixels from each of the first plurality of images, wherein theannotations comprise an indication of a region of a retina; resamplingdata for the first plurality of pixels from said each of the firstplurality of images to generate a second plurality of imagescorresponding to images that would be acquired with a second OCT systemperforming a scan of the plurality of retinas with a second scan patterndifferent from the first scan pattern; and training a neural networkusing the second plurality of images and the annotations.

Clause 39. The method of clause 38, further comprising aligning theresampled data using the annotations.

Clause 40. The method of clause 39, further comprising generatingadditional training data for the neural network by augmenting ordegenerating the first plurality of images prior to resampling the datafor the first plurality of pixels and using the annotations to align theresampled data.

Clause 41. The method of clause 40, wherein augmenting the firstplurality of images further comprises one or more of applying curving,horizontal flip, X-roll, Y-scale, Y-translate, elastic transformation orGamma contrast to the first plurality of images.

Clause 42. The method of clause 40, wherein degenerating the firstplurality of images further comprises applying one or more ofresampling, down sampling, speckle noise, Y-Gaussian blur or A-ScanY-jitter to the first plurality of images.

Clause 43. The method of clause 38, wherein the first scan pattern is alinear scan pattern and the second scan pattern comprises a plurality oflobes.

Clause 44. A method of processing data obtained from an OCT system,comprising: obtaining a first plurality of interferograms, wherein eachof the interferograms corresponds to data acquired by a first OCT systemperforming a scan of a retina using a first scan pattern; annotatingeach of the first plurality of interferograms formed from the dataacquired using the first scan pattern to indicate a tissue structure ofthe retina; training a neural network using the first plurality ofinterferograms and the annotations; inputting a second plurality ofinterferograms into the trained neural network, the second plurality ofinterferograms corresponding to data acquired by a second OCT systemperforming a scan of a retina using a second scan pattern; and obtainingan output from the trained neural network, the output indicating thetissue structure of the retina that was scanned using the second scanpattern.

Clause 45. The method of clause 44, wherein the first scan patterncomprises a linear scan pattern and the second scan pattern comprises acurved scan pattern.

Clause 46. The method of clause 45, wherein the linear scan patterncomprises one or more of a radial scan pattern or a raster scan patternand wherein the curved scan pattern comprises a plurality of lobes.

Clause 47. The method of clause 45, wherein the first plurality ofinterferograms corresponds to a B-scan of the retina along the firstscan pattern and the second plurality of interferograms comprises aplurality of A-scans of the retina arranged along the curved scanpattern.

Clause 48. The method of clause 44, wherein the tissue structurecomprises one or more of an inner limiting membrane (ILM) or a retinalpigment epithelium (RPE).

Clause 49. The method of clause 44, wherein the neural network comprisesa convolutional neural network.

Clause 50. The method of clause 44, wherein the second scan patterncomprises a rose curve.

Clause 51. The method of clause 44, further comprising: generatingadditional training data for the neural network based on the firstplurality of interferograms by performing one or more processingoperations on one or more of the first plurality of interferograms, theone or more processing operations comprising one or more of randomhorizontal flipping, random shifting in the x direction, random scalingalong an axis, random translation along a direction, a blurringoperation, or a variable elastic transformation; annotating theadditional training data based on the annotations of the one or more ofthe first plurality of interferograms to which were applied theprocessing operations; and training the neural network using theadditional training data and the annotations for the additional trainingdata.

Clause 52. The method of clause 44, further comprising training theneural network using data comprising the first plurality ofinterferograms and the annotations based on the first scan pattern anddata comprising the second plurality of interferograms and annotationsfor the second plurality of interferograms based on the second scanpattern.

Clause 53. The method of clause 52, further comprising prior to trainingthe neural network, processing the second plurality of interferograms toproduce interferograms that correspond to the first plurality ofinterferograms.

Clause 54. The method of clause 53, wherein the first scan patterncomprises a linear scan pattern and the second scan pattern comprises aplurality of lobes, and wherein processing the second plurality ofinterferograms comprises interpolating the data acquired from the secondscan pattern to produce data corresponding to the linear scan pattern.

Clause 55. The method of clause 51, wherein the blurring operation isperformed using a Gaussian blur operation.

Clause 56. The method of clause 52, wherein each of the first pluralityof interferograms based on the first scan pattern and a correspondingone of the second plurality of interferograms based on the second scanpattern are obtained from scans on the same retina.

Clause 57. The method of clause 44, wherein the first plurality ofinterferograms based on the first scan pattern comprise a higherresolution scan having a resolution associated with a smaller resolvablefeature size than the second plurality of interferograms based on thesecond scan pattern.

Clause 58. The method of clause 57, wherein the first scan patterncomprises a plurality of linear scans and the second scan patterncomprises a plurality of lobes.

Clause 59. The method of clause 58, wherein prior to using the firstplurality of interferograms to train the neural network, each of thefirst plurality of interferograms is subjected to a blurring operation.

Clause 60. The method of clause 44, wherein the first scan patterncomprises a linear scan pattern and the second scan pattern comprises aplurality of lobes, and prior to inputting the second plurality ofinterferograms, the method further comprises interpolating the dataacquired from the second scan pattern to produce data that would resultfrom a linear scan pattern.

Clause 61. The method of clause 60, further comprising: generating a setof input data from the second scan pattern, with each of the setcomprising interpolated data representing a radial scan of a retina fora specific plane; and combining the outputs of the trained neuralnetwork to form a 3D image of the retina.

Clause 62. The method of clause 49, wherein the convolutional neuralnetwork comprises a U-Net architecture that comprises a plurality ofconvolutional neural network layers.

Clause 63. The method of clause 49, wherein the convolutional neuralnetwork comprises a contractor path and an expansion path, theconvolutional neural network configured to exchange spatial featureswith semantic values along the contractor path and to exchange thesemantic features with the spatial features along the expansion path.

Clause 64. The method of clause 44, wherein the neural network comprisesa plurality of semantic feature channels corresponding to an ILM layerand an RPE layer of a retina.

Clause 65. The method of clause 44, wherein the first plurality ofinterferograms comprises a B-scan image and the output of the trainedneural network comprises a B-scan image that would be obtained with datafrom the second scanning pattern, the second scanning pattern differentfrom the first scanning pattern.

Clause 66. The method of clause 49, wherein the convolution neuralnetwork comprises a number of convolutional layers within a range fromabout 10 to about 40, a number of biases and weights within a range fromabout 1 million to about 4 million and a number of semantic featurechannels within a range from about 10 to about 500.

Clause 67. The method of clause 44, wherein the first plurality ofinterferograms comprises an axial resolution within a range from about 1micron to about 5 microns and wherein the second plurality ofinterferograms comprises an axial resolution within a range from about 6microns to about 30 microns.

Clause 68. The method of clause 44, wherein the first scan patterncomprises a linear scan pattern and the second scan pattern comprisesthe linear scan pattern.

Clause 69. The method of clause 44, wherein the first scan patterncomprises a curved scan pattern and the second scan pattern comprisesthe curved scan pattern.

Clause 70. A method of processing an image of a retina, comprising:receiving a plurality of A-scans corresponding to a plurality oflocations along an OCT scan pattern; inputting the plurality of A-scansinto a trained neural network; and outputting a segmented image from thetrained neural network corresponding to the plurality of locations alongthe OCT scan pattern, the segmented image comprising an identificationof one or more of a boundary of an ILM layer, a boundary of an RPElayer, or a boundary of a pool of fluid within the retina.

Clause 71. The method of clause 70, wherein the plurality of A-scans isinterpolated to generate a plurality of B-scan images and wherein theplurality of B-scan images is input into a convolutional neural networkto generate a plurality of segmented B-scan images, and wherein theplurality of segmented B-scan images is interpolated to generate thesegmented image corresponding to the plurality of locations along theOCT scan pattern.

Clause 72. The method of clause 70, wherein the OCT scan patterncomprises a curved scan pattern and wherein the plurality of A-scansalong the curved scan pattern is input into a trained convolutionalneural network configured to output the segmented image, the segmentedimage comprising a plurality of segmented A-scans corresponding to theplurality of locations along the curved scan pattern.

Clause 73. The method of clause 72, wherein the convolutional neuralnetwork comprises a contractor path and an expansion path, theconvolutional neural network configured to exchange spatial featureswith semantic values along the contractor path and to exchange thesemantic features with the spatial features along the expansion path.

Clause 74. The method of clause 72, wherein the convolutional neuralnetwork comprises a number of convolutional layers within a range fromabout 10 to about 40, a number of biases and weights within a range fromabout 1 million to about 4 million and a number of semantic featurechannels within a range from about 10 to about 500.

Clause 75. The method of clause 70, further comprising: processing theplurality of A-scans to generate a B-scan image, with the B-scan imagecorresponding to a radial scan of a retina for a specific plane;inputting the B-scan image into a convolutional neural network, whereinthe convolutional neural network outputs the segmented image; repeatingthe processing and inputting steps for multiple pluralities of A-scanswith each of the multiple pluralities corresponding to a differentplane; and combining the outputs of the convolutional neural network toform a 3D image of the retina.

Clause 76. The method of clause 75, wherein processing the plurality ofA-scans to generate a B-scan image further comprises interpolating datafrom the A-scans.

Clause 77. A method of processing an OCT image, comprising: receivingthe OCT image; inputting the received OCT image into a trained neuralnetwork; and receiving a segmented image as output from the trainedneural network, the segmented image corresponding to the input OCT imageand comprising an identification of one or more of a boundary of an ILMlayer, a boundary of an RPE layer, or a boundary of a pool of fluidwithin the retina.

Clause 78. The method of clause 77, wherein the neural network istrained using a set of training data and a corresponding set ofannotations for the set of training data.

Clause 79. The method of clause 78, wherein the set of training datacomprises a plurality of OCT images obtained using a first scan pattern.

Clause 80. The method of clause 79, wherein the training data furthercomprises a set of augmented images generated from the plurality of OCTimages.

Clause 81. The method of clause 80, wherein the set of augmented imagesis generated by applying one or more of curving, horizontal flip,X-roll, Y-scale, Y-translate, elastic transformation or Gamma contrastto the plurality of OCT images.

Clause 82. The method of clause 79, wherein the training data furthercomprises a set of degenerated images generated from the plurality ofOCT images.

Clause 83. The method of clause 82, wherein the set of degeneratedimages is generated by applying one or more of resampling, downsampling, speckle noise, Y-Gaussian blur or A-Scan Y-jitter to theplurality of OCT images.

Clause 84. The method of clause 79, wherein the training data furthercomprises a second plurality of OCT images obtained by resampling theplurality of images obtained using the first scan pattern to produce aplurality of images based on a second scan pattern.

Clause 85. The method of clause 84, wherein the first scan pattern is alinear scan pattern and the second scan pattern comprises a plurality oflobes.

Clause 86. An apparatus, comprising: a set of computer-executableinstructions; a processor configured with the set of computer-executableinstructions, wherein when executed by the processor, the set ofinstructions cause the processor to perform the method any one of thepreceding clauses.

Embodiments of the present disclosure have been shown and described asset forth herein and are provided by way of example only. One ofordinary skill in the art will recognize numerous adaptations, changes,variations and substitutions without departing from the scope of thepresent disclosure. Several alternatives and combinations of theembodiments disclosed herein may be utilized without departing from thescope of the present disclosure and the inventions disclosed herein.Therefore, the scope of the presently disclosed inventions shall bedefined solely by the scope of the appended claims and the equivalentsthereof.

What is claimed is:
 1. A method of processing data obtained from an OCTsystem, the method comprising: obtaining a first plurality of images,wherein each of the first plurality of images corresponds to dataacquired by a first OCT system performing a scan of a retina; annotatinga plurality of pixels from each of the first plurality of images togenerate segmented image data of the retina, wherein the annotationidentifies one or more structures of the retina; generating a pluralityof degenerated images from the first plurality of images by degeneratingthe first plurality of images; and training a neural network using theplurality of degenerated images and the segmented image data; whereinthe plurality of degenerated images comprises a plurality of images ofan increasing difficulty, the increasing difficulty comprising one ormore of a linearly increasing difficulty, or a random difficulty abovean increasing threshold of difficulty and wherein the increasingdifficulty increases toward a difficulty of images from a second OCTsystem, the second OCT system comprising a lower resolution than thefirst OCT system.
 2. The method of claim 1, wherein the segmented imagedata comprises a plurality of segmented images, each of the plurality ofsegmented images comprising an annotation defining a class for eachpixel of said each of the plurality of images.
 3. The method of claim 2,wherein the plurality of segmented images comprises a first plurality ofsegmented images corresponding to the first plurality of images and asecond plurality of segmented images corresponding to the plurality ofdegenerated images.
 4. The method of claim 3, wherein generating theplurality of degenerated images comprises applying a transform functionto the first plurality of images to cause a geometric transformation ofthe first plurality of images, and wherein the transform function isapplied to the first plurality of segmented images to obtain the secondplurality of segmented images.
 5. The method of claim 3, wherein each ofthe first plurality of segmented images comprises annotations at firstlocations for each of a first plurality of pixels of the first pluralityof segmented images and wherein each of the second plurality ofsegmented images comprises annotations at second locations for each of asecond plurality of pixels of the second plurality of segmented images.6. The method of claim 3, further comprising: generating a firstplurality of geometrically transformed segmented images by applying ageometric transform function to the first plurality of segmented images;and generating a second plurality of geometrically transformed segmentedimages by applying the geometric transform function to the secondplurality of segmented images.
 7. The method of claim 1, wherein, theone or more structures of the retina comprise background, retinal nervefiber layer, ganglion cell layer and inner plexiform layer, outerplexiform layer and inner nuclear layer, outer nuclear layer andexternal limiting membrane, retinal pigment epithelium andphotoreceptors, chorio-capillaries and chorio-septae.
 8. The method ofclaim 1, wherein the plurality of degenerated images comprises augmentedimages and wherein the augmented images are generated by applying ageometric transform to the first plurality of images and wherein thegeometric transform comprises one or more of curving, horizontal flip,X-roll, Y-scale, Y-translate, or elastic transformation.
 9. The methodof claim 1, wherein the first OCT system comprises a first configurationand wherein the plurality of degenerated images and segmented image datacomprise a transfer learning data set configured to train the neuralnetwork to classify data from the second OCT system, the second OCTsystem comprising a second configuration different from the firstconfiguration of the first OCT system.
 10. The method of claim 1,wherein the first plurality of images corresponds to a first resolutionof the first OCT system and wherein the plurality of degenerated imagescorresponds to images of the second OCT system having a secondresolution, wherein the first resolution is associated with a smallerresolvable feature size than the second resolution.
 11. The method ofclaim 1, wherein the first OCT system comprises a first configuration,and wherein the neural network, after training, is used to classify datafrom the second OCT system, the second OCT system comprising a secondconfiguration different from the first configuration, and wherein thefirst configuration differs from the second configuration with regardsto one or more of an axial resolution, a scan pattern, or a lateralresolution.
 12. The method of claim 11, wherein the neural network isnot trained with data from the second OCT system.
 13. The method ofclaim 11, wherein the first configuration of the first OCT systemcomprises a first resolution and the second configuration of the secondOCT system comprises a second resolution, and wherein the firstresolution is associated with a smaller resolvable feature size than thesecond resolution.
 14. The method of claim 11, wherein the neuralnetwork is trained with a transfer learning dataset, the transferlearning data set comprising first degenerated and augmented OCT imagesfrom the first OCT system and corresponding annotated OCT images fromthe first OCT system.
 15. The method of claim 14, wherein the transferlearning dataset comprises second OCT images from the second OCT systemand corresponding annotated OCT images from the second OCT system. 16.The method of claim 14, wherein the transfer learning dataset is derivedfrom 1) resampled and annotated OCT image data from the first OCTsystem, 2) resampled, degenerated, and augmented OCT image data from thefirst OCT system; and 3) OCT image data and annotation data from thesecond OCT system.
 17. The method of claim 14, wherein the transferlearning dataset comprises OCT data from a plurality of eyes and whereineach of the plurality of eyes is measured with the first OCT system andwith the second OCT system.
 18. The method of claim 1, wherein adifficulty of a next degenerated image is determined from resampledimage data, and the next degenerated image is generated in response tothe difficulty, the resampled image data generated by resampling thefirst plurality of images.
 19. The method of claim 1, wherein theincreasing difficulty comprises a combination of the linearly increasingdifficulty and the random difficulty.
 20. The method of claim 1, whereinthe first plurality of images comprises one or more pools ofintraretinal fluid not visible in the plurality of degenerated imagesand wherein the one or more pools of intraretinal fluids is visible in asegmented OCT image generated with the trained neural network from theplurality of degenerated images.
 21. The method of claim 1, wherein thescan pattern comprises a linear scan pattern and the plurality ofdegenerated images corresponds to second scan pattern comprising aplurality of lobes.